Overview

Dataset statistics

Number of variables127
Number of observations23
Missing cells220
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 KiB
Average record size in memory1021.6 B

Variable types

Numeric6
Categorical59
Boolean57
Unsupported5

Alerts

Prescritpion has constant value "False" Constant
Lab-Report has constant value "False" Constant
X-ray has constant value "False" Constant
2. On a scale of 1-5, how much fatigue do you feel after having your meals? has constant value "1.0" Constant
1. On an average how many cups of tea/coffee do you have per day? has constant value "0.0" Constant
2. Do you have any variation in blood pressure? High BP/Low BP- (if the client has answered the qn no 21 in ‘Profile’, this question can be made hidden.) has constant value "True" Constant
12. Do you have difficulty in digesting fruits and vegetables; undigested food found in stools? Yes/No has constant value "False" Constant
13. Do you have excessive burping/ belching/ bloating? Yes/No has constant value "False" Constant
16. Do you experience tremor/shaking while your legs or hands are at rest ? Yes/No has constant value "False" Constant
6. Do you have any increase in fat distribution around the chest and hips? Yes/No has constant value "False" Constant
4. Present Medications? has constant value "True" Constant
(IF SPORT PERSON) has constant value "True" Constant
(IF ATHLETE) has constant value "nil" Constant
Do you exercise regularly has constant value "True" Constant
Unnamed: 0.1 is highly correlated with Unnamed: 0 and 61 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Unnamed: 0.1 and 45 other fieldsHigh correlation
Height is highly correlated with Unnamed: 0 and 24 other fieldsHigh correlation
Weight is highly correlated with Unnamed: 0.1 and 42 other fieldsHigh correlation
6. On a scale of 1-5, how often do you feel mentally sluggish these days? is highly correlated with Name and 10 other fieldsHigh correlation
8. On a scale of 1-5, how much do you feel sleepy during the day? is highly correlated with Unnamed: 0.1 and 31 other fieldsHigh correlation
D. Days of flow is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
(IF FITNESS ENTHUSIAST) is highly correlated with Unnamed: 0.1 and 7 other fieldsHigh correlation
5. Do you have any Family History of the following diseases? is highly correlated with Unnamed: 0.1 and 5 other fieldsHigh correlation
7. On a scale of 1-5, how much do you feel difficulty in focussing or difficulty in concentrating? is highly correlated with Name and 4 other fieldsHigh correlation
3. Are you suffering from Pimples/Acne? is highly correlated with Name and 6 other fieldsHigh correlation
9. How will you describe your skin type? is highly correlated with Unnamed: 0.1 and 25 other fieldsHigh correlation
2. How often do you skip your meals? is highly correlated with Name and 5 other fieldsHigh correlation
19. Do you feel difficulty in spelling correctly, familiar words? Yes/No is highly correlated with Unnamed: 0.1 and 12 other fieldsHigh correlation
20. Has there any change in vision (eye sight) recently ? Yes/No is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
23. Are you more emotional now-a days, than in the past? is highly correlated with Name and 6 other fieldsHigh correlation
13. Do you have excessive burping/ belching/ bloating? Yes/No is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
15. Do you sweat/perspire excessively, with little or no activity? Yes/No is highly correlated with Name and 12 other fieldsHigh correlation
Occupation is highly correlated with Unnamed: 0.1 and 19 other fieldsHigh correlation
Uric Acid is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
F. Clots is highly correlated with Unnamed: 0.1 and 19 other fieldsHigh correlation
24. Does your frequency of urination increased these days? is highly correlated with Unnamed: 0.1 and 6 other fieldsHigh correlation
2. Do you feel it is difficult to be attentive in your studies? is highly correlated with Unnamed: 0.1 and 6 other fieldsHigh correlation
4. On a scale of 1-5 how will you rate your appetite on a normal day? is highly correlated with Unnamed: 0.1 and 8 other fieldsHigh correlation
Ethinicity is highly correlated with Unnamed: 0.1 and 18 other fieldsHigh correlation
Do you exercise regularly is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
1. Do you feel any urgency in urination, these days? Yes/No is highly correlated with Name and 8 other fieldsHigh correlation
Blood pressure is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
12. Do you have difficulty in digesting fruits and vegetables; undigested food found in stools? Yes/No is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
Gender is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
2. Do you feel any decrease in fullness of erections? Yes/No is highly correlated with Name and 8 other fieldsHigh correlation
5. Do you have recurrent premature ejaculations? is highly correlated with Name and 8 other fieldsHigh correlation
3. How often do you feel sleeplessness at night is highly correlated with Name and 11 other fieldsHigh correlation
HDL is highly correlated with Unnamed: 0.1 and 25 other fieldsHigh correlation
5. Do you feel any hot flushes? Yes/No is highly correlated with Name and 1 other fieldsHigh correlation
How many hours per week is highly correlated with Unnamed: 0.1 and 22 other fieldsHigh correlation
7. Are you suffering from any of the following symptoms, these days? is highly correlated with Name and 3 other fieldsHigh correlation
Name is highly correlated with Unnamed: 0.1 and 106 other fieldsHigh correlation
11. Do you have difficulty in gaining/losing weight ? Yes/No; If yes→ for gaining/losing is highly correlated with Name and 1 other fieldsHigh correlation
TSH is highly correlated with Unnamed: 0.1 and 18 other fieldsHigh correlation
1. On an average how many cups of tea/coffee do you have per day? is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
G. Abnormal discharges is highly correlated with Unnamed: 0.1 and 9 other fieldsHigh correlation
Alcohol intake is highly correlated with Unnamed: 0.1 and 12 other fieldsHigh correlation
Skin allergies is highly correlated with Unnamed: 0.1 and 3 other fieldsHigh correlation
1. Do you feel trembling or palpitation, recurrently? Yes/No is highly correlated with Name and 3 other fieldsHigh correlation
11. How do you describe yourself? is highly correlated with Unnamed: 0.1 and 26 other fieldsHigh correlation
Triglyceride is highly correlated with Unnamed: 0.1 and 20 other fieldsHigh correlation
4. Are you prone to fat deposition in the abdominal area? Yes/No is highly correlated with Name and 4 other fieldsHigh correlation
1. Are you suffering from any lifestyle diseases of the following? is highly correlated with Unnamed: 0.1 and 3 other fieldsHigh correlation
2. Do you have any variation in blood pressure? High BP/Low BP- (if the client has answered the qn no 21 in ‘Profile’, this question can be made hidden.) is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
Place is highly correlated with Unnamed: 0.1 and 29 other fieldsHigh correlation
10. Do you sweat too much while sleeping ? Yes/No is highly correlated with Name and 12 other fieldsHigh correlation
C. Regularity is highly correlated with Unnamed: 0 and 7 other fieldsHigh correlation
5. On a scale of 1-5 how much do you feel sluggish or less energetic(physically) these days? is highly correlated with Unnamed: 0.1 and 6 other fieldsHigh correlation
10. Are you suffering from any of the following?(Depression; Anxiety; ADHD; Bipolar Disorder; Schizophrenia etc) is highly correlated with Unnamed: 0.1 and 17 other fieldsHigh correlation
4. Are you suffering from severe mood swings? Yes/No is highly correlated with Unnamed: 0.1 and 4 other fieldsHigh correlation
Urea is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
9. Do you have excessive hair fall, these days? Yes/No is highly correlated with Name and 3 other fieldsHigh correlation
Salt is highly correlated with Unnamed: 0 and 12 other fieldsHigh correlation
ESR is highly correlated with Unnamed: 0.1 and 17 other fieldsHigh correlation
6. How often do you experience mood swings? is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
H. Associated symptoms- Pains/PMS/cramps/Low Back pain/ Mood Swings/Depressed is highly correlated with Unnamed: 0.1 and 18 other fieldsHigh correlation
Lab-Report is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
17. Do you feel any giddiness on standing for a long time? Yes/No is highly correlated with Name and 6 other fieldsHigh correlation
7. How often do you feel agitated or easily upset or nervous? is highly correlated with Name and 1 other fieldsHigh correlation
Allergy is highly correlated with Name and 3 other fieldsHigh correlation
7. How will you describe your ‘sex drive’ (Age limited) is highly correlated with Unnamed: 0.1 and 25 other fieldsHigh correlation
21. Is there any difficulty in making decisions or co-ordinating ? is highly correlated with Unnamed: 0.1 and 19 other fieldsHigh correlation
Prescritpion is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
SGOT is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
X-ray is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
4. How much time do you sleep in a day is highly correlated with Unnamed: 0 and 34 other fieldsHigh correlation
Fasting blood sugar is highly correlated with Unnamed: 0.1 and 16 other fieldsHigh correlation
A. Age of Menarche is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
4. How will you describe yourself? Sluggish/Active/Hyperactive is highly correlated with Unnamed: 0 and 22 other fieldsHigh correlation
2. On a scale of 1-5, how much fatigue do you feel after having your meals? is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
SGPT is highly correlated with Unnamed: 0.1 and 17 other fieldsHigh correlation
4. Do you feel any decrease in physical stamina? Yes/No is highly correlated with Name and 8 other fieldsHigh correlation
18. Do you feel your memory is poor or are you forgetful? Yes/No is highly correlated with Name and 5 other fieldsHigh correlation
3. On a scale of 1-5 how will you rate your thirst on a normal day? is highly correlated with Name and 5 other fieldsHigh correlation
6. Do you experience any changes in your body weight, when you are under stress for a certain period? Yes/No → Weight Gain/Weight Los is highly correlated with Name and 1 other fieldsHigh correlation
8. How often do you exercise? is highly correlated with Unnamed: 0.1 and 24 other fieldsHigh correlation
3. Is your waist circumference equal or larger than hip circumference? Yes/No is highly correlated with Name and 3 other fieldsHigh correlation
Diet is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
3. Do you feel sleepy during study time? is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
5. Are you under a high amount of stress? Yes/No is highly correlated with Name and 2 other fieldsHigh correlation
8. Do you have any Drug allergies? is highly correlated with Name and 3 other fieldsHigh correlation
6. Have you undergone any major surgery(ies)? Yes/No; if yes please specify is highly correlated with Unnamed: 0.1 and 3 other fieldsHigh correlation
8. Do you have difficulty in bowel movements/constipation? Yes/No is highly correlated with Name High correlation
1.On a scale of 1-5, how much do you feel irritable if there is a delay in food or if a meal is missed? is highly correlated with Name and 2 other fieldsHigh correlation
4. Present Medications? is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
(IF SPORT PERSON) is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
Smoking Cigars is highly correlated with Unnamed: 0.1 and 12 other fieldsHigh correlation
2. Do you have any bleeding after menopause? Yes/No is highly correlated with Unnamed: 0.1 and 13 other fieldsHigh correlation
5. How often do you take fried foods/junk foods? is highly correlated with Unnamed: 0.1 and 7 other fieldsHigh correlation
9. Are you having any form of physical disability? is highly correlated with Name and 3 other fieldsHigh correlation
16. Do you experience tremor/shaking while your legs or hands are at rest ? Yes/No is highly correlated with D. Days of flow and 112 other fieldsHigh correlation
22. Do you feel depressed or lack motivation? is highly correlated with Name and 5 other fieldsHigh correlation
14. Do you have difficulty in learning new things ? Yes/No is highly correlated with Name and 1 other fieldsHigh correlation
E. Quantity of bleeding- Heavy/Normal/scanty is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
Preffered cooking oil is highly correlated with Unnamed: 0 and 38 other fieldsHigh correlation
Creatinine is highly correlated with Unnamed: 0.1 and 28 other fieldsHigh correlation
4. Are you suffering from hair loss? is highly correlated with Unnamed: 0.1 and 4 other fieldsHigh correlation
1. Do you feel forgetfulness, these days? is highly correlated with Unnamed: 0.1 and 10 other fieldsHigh correlation
3. Present system of Treatment? is highly correlated with Unnamed: 0.1 and 5 other fieldsHigh correlation
Hb is highly correlated with Unnamed: 0.1 and 25 other fieldsHigh correlation
Sweet is highly correlated with Unnamed: 0.1 and 8 other fieldsHigh correlation
7. Are you gaining weight even with a low calorie diet? Yes/No is highly correlated with Name and 1 other fieldsHigh correlation
Addiction(Others if any) is highly correlated with Unnamed: 0.1 and 12 other fieldsHigh correlation
2. Do you have any abnormal hair growth? (eg: in face, chest etc) is highly correlated with Name High correlation
Marital Status is highly correlated with Unnamed: 0.1 and 18 other fieldsHigh correlation
Total Cholesterol is highly correlated with Unnamed: 0.1 and 16 other fieldsHigh correlation
3. Do you have any difficulty in maintaining morning erections? Yes/No is highly correlated with Name and 8 other fieldsHigh correlation
B. Cycle length is highly correlated with Unnamed: 0.1 and 25 other fieldsHigh correlation
3. Do you have any disinterest in sex? Yes/No is highly correlated with Unnamed: 0.1 and 14 other fieldsHigh correlation
2. How long are you suffering? is highly correlated with Unnamed: 0.1 and 5 other fieldsHigh correlation
HbA1C is highly correlated with Unnamed: 0.1 and 18 other fieldsHigh correlation
LDL is highly correlated with Unnamed: 0.1 and 17 other fieldsHigh correlation
Age is highly correlated with Unnamed: 0.1 and 12 other fieldsHigh correlation
BMI is highly correlated with Unnamed: 0.1 and 19 other fieldsHigh correlation
Diet has 1 (4.3%) missing values Missing
Allergy has 5 (21.7%) missing values Missing
Prescritpion has 13 (56.5%) missing values Missing
Lab-Report has 13 (56.5%) missing values Missing
X-ray has 13 (56.5%) missing values Missing
4. How much time do you sleep in a day has 4 (17.4%) missing values Missing
7. How will you describe your ‘sex drive’ (Age limited) has 8 (34.8%) missing values Missing
9. How will you describe your skin type? has 4 (17.4%) missing values Missing
6. Do you have any increase in fat distribution around the chest and hips? Yes/No has 22 (95.7%) missing values Missing
MENOPAUSAL FEMALES ONLY has 23 (100.0%) missing values Missing
1. Age of Menopause has 23 (100.0%) missing values Missing
What kind of sports has 23 (100.0%) missing values Missing
(IF ATHLETE) has 22 (95.7%) missing values Missing
Participating category(ies) has 23 (100.0%) missing values Missing
Kind of exercises you prefer(yoga/weight training/Strength Training/Functional Movements/cardio/cross training etc) has 23 (100.0%) missing values Missing
Name is uniformly distributed Uniform
Unnamed: 0.1 has unique values Unique
Unnamed: 0 has unique values Unique
Name has unique values Unique
MENOPAUSAL FEMALES ONLY is an unsupported type, check if it needs cleaning or further analysis Unsupported
1. Age of Menopause is an unsupported type, check if it needs cleaning or further analysis Unsupported
What kind of sports is an unsupported type, check if it needs cleaning or further analysis Unsupported
Participating category(ies) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Kind of exercises you prefer(yoga/weight training/Strength Training/Functional Movements/cardio/cross training etc) is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-10-25 14:33:53.761752
Analysis finished2022-10-25 14:37:55.044676
Duration4 minutes and 1.28 second
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487.3913043
Minimum32
Maximum1030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:07:55.359915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile70.3
Q1181.5
median521
Q3698.5
95-th percentile999.4
Maximum1030
Range998
Interquartile range (IQR)517

Descriptive statistics

Standard deviation320.3257914
Coefficient of variation (CV)0.657225085
Kurtosis-1.199417623
Mean487.3913043
Median Absolute Deviation (MAD)298
Skewness0.2344490196
Sum11210
Variance102608.6126
MonotonicityStrictly increasing
2022-10-25T20:07:55.709596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
321
 
4.3%
5271
 
4.3%
10041
 
4.3%
9581
 
4.3%
9101
 
4.3%
8071
 
4.3%
7241
 
4.3%
6731
 
4.3%
6661
 
4.3%
5631
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
321
4.3%
661
4.3%
1091
4.3%
1511
4.3%
1641
4.3%
1731
4.3%
1901
4.3%
2231
4.3%
2821
4.3%
3731
4.3%
ValueCountFrequency (%)
10301
4.3%
10041
4.3%
9581
4.3%
9101
4.3%
8071
4.3%
7241
4.3%
6731
4.3%
6661
4.3%
5631
4.3%
5521
4.3%

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean920.3043478
Minimum32
Maximum4072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:07:56.077420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile70.3
Q1181.5
median523
Q3702
95-th percentile3686.9
Maximum4072
Range4040
Interquartile range (IQR)520.5

Descriptive statistics

Standard deviation1209.131598
Coefficient of variation (CV)1.313838841
Kurtosis2.103570255
Mean920.3043478
Median Absolute Deviation (MAD)333
Skewness1.830531214
Sum21167
Variance1461999.221
MonotonicityStrictly increasing
2022-10-25T20:07:56.434054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
321
 
4.3%
5301
 
4.3%
37401
 
4.3%
32091
 
4.3%
24961
 
4.3%
11241
 
4.3%
7281
 
4.3%
6761
 
4.3%
6691
 
4.3%
5661
 
4.3%
Other values (13)13
56.5%
ValueCountFrequency (%)
321
4.3%
661
4.3%
1091
4.3%
1511
4.3%
1641
4.3%
1731
4.3%
1901
4.3%
2231
4.3%
2831
4.3%
3741
4.3%
ValueCountFrequency (%)
40721
4.3%
37401
4.3%
32091
4.3%
24961
4.3%
11241
4.3%
7281
4.3%
6761
4.3%
6691
4.3%
5661
4.3%
5551
4.3%

Name
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
arya tara rai
 
1
shreyanesh grover
 
1
viraj vishal bekellu_gfx0205687
 
1
jiya r rakholiya _gfx0205683
 
1
laxmi-stb210022567
 
1
Other values (18)
18 

Length

Max length31
Median length19
Mean length16.04347826
Min length8

Characters and Unicode

Total characters369
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st rowarya tara rai
2nd rowsriya sreenivasan
3rd rowshitanshu verma
4th rowrizanizar
5th rowiffa ahmed

Common Values

ValueCountFrequency (%)
arya tara rai1
 
4.3%
shreyanesh grover1
 
4.3%
viraj vishal bekellu_gfx02056871
 
4.3%
jiya r rakholiya _gfx02056831
 
4.3%
laxmi-stb2100225671
 
4.3%
nemy joseph1
 
4.3%
k shanker1
 
4.3%
riyana parveen p s1
 
4.3%
miss senora menezes1
 
4.3%
ved dharmendra dhanani1
 
4.3%
Other values (13)13
56.5%

Length

2022-10-25T20:07:56.794409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sriya2
 
3.8%
sreenivasan2
 
3.8%
k2
 
3.8%
arya1
 
1.9%
gurung1
 
1.9%
johnson1
 
1.9%
druvik1
 
1.9%
rakesh1
 
1.9%
annette1
 
1.9%
adarsh1
 
1.9%
Other values (40)40
75.5%

Most occurring characters

ValueCountFrequency (%)
a49
13.3%
34
 
9.2%
r33
 
8.9%
e31
 
8.4%
s30
 
8.1%
i24
 
6.5%
n23
 
6.2%
h17
 
4.6%
k11
 
3.0%
v10
 
2.7%
Other values (24)107
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter309
83.7%
Space Separator34
 
9.2%
Decimal Number23
 
6.2%
Connector Punctuation2
 
0.5%
Dash Punctuation1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a49
15.9%
r33
10.7%
e31
10.0%
s30
9.7%
i24
 
7.8%
n23
 
7.4%
h17
 
5.5%
k11
 
3.6%
v10
 
3.2%
m9
 
2.9%
Other values (13)72
23.3%
Decimal Number
ValueCountFrequency (%)
06
26.1%
25
21.7%
53
13.0%
63
13.0%
72
 
8.7%
82
 
8.7%
31
 
4.3%
11
 
4.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin309
83.7%
Common60
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a49
15.9%
r33
10.7%
e31
10.0%
s30
9.7%
i24
 
7.8%
n23
 
7.4%
h17
 
5.5%
k11
 
3.6%
v10
 
3.2%
m9
 
2.9%
Other values (13)72
23.3%
Common
ValueCountFrequency (%)
34
56.7%
06
 
10.0%
25
 
8.3%
53
 
5.0%
63
 
5.0%
72
 
3.3%
_2
 
3.3%
82
 
3.3%
31
 
1.7%
-1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a49
13.3%
34
 
9.2%
r33
 
8.9%
e31
 
8.4%
s30
 
8.1%
i24
 
6.5%
n23
 
6.2%
h17
 
4.6%
k11
 
3.0%
v10
 
2.7%
Other values (24)107
29.0%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.130434783
Minimum5
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:07:57.102132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q17
median8
Q310
95-th percentile11
Maximum11
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.984126733
Coefficient of variation (CV)0.2440369779
Kurtosis-1.140842544
Mean8.130434783
Median Absolute Deviation (MAD)2
Skewness-0.08248193814
Sum187
Variance3.936758893
MonotonicityNot monotonic
2022-10-25T20:07:57.690078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
105
21.7%
85
21.7%
74
17.4%
53
13.0%
113
13.0%
62
 
8.7%
91
 
4.3%
ValueCountFrequency (%)
53
13.0%
62
 
8.7%
74
17.4%
85
21.7%
91
 
4.3%
105
21.7%
113
13.0%
ValueCountFrequency (%)
113
13.0%
105
21.7%
91
 
4.3%
85
21.7%
74
17.4%
62
 
8.7%
53
13.0%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
f
14 
m

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowf
3rd rowm
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Length

2022-10-25T20:07:58.008793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:07:58.357871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Most occurring characters

ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter23
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Most occurring scripts

ValueCountFrequency (%)
Latin23
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII23
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f14
60.9%
m9
39.1%

Place
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
17 
north india
darjeeling
 
1
banglore
 
1
south india
 
1

Length

Max length11
Median length3
Mean length4.869565217
Min length3

Characters and Unicode

Total characters112
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st rowdarjeeling
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil17
73.9%
north india2
 
8.7%
darjeeling1
 
4.3%
banglore 1
 
4.3%
south india1
 
4.3%
karnataka1
 
4.3%

Length

2022-10-25T20:07:58.702518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:07:59.070208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil17
65.4%
india3
 
11.5%
north2
 
7.7%
darjeeling1
 
3.8%
banglore1
 
3.8%
south1
 
3.8%
karnataka1
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n25
22.3%
i24
21.4%
l19
17.0%
a9
 
8.0%
r5
 
4.5%
4
 
3.6%
d4
 
3.6%
t4
 
3.6%
o4
 
3.6%
h3
 
2.7%
Other values (7)11
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108
96.4%
Space Separator4
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n25
23.1%
i24
22.2%
l19
17.6%
a9
 
8.3%
r5
 
4.6%
d4
 
3.7%
t4
 
3.7%
o4
 
3.7%
h3
 
2.8%
e3
 
2.8%
Other values (6)8
 
7.4%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin108
96.4%
Common4
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n25
23.1%
i24
22.2%
l19
17.6%
a9
 
8.3%
r5
 
4.6%
d4
 
3.7%
t4
 
3.7%
o4
 
3.7%
h3
 
2.8%
e3
 
2.8%
Other values (6)8
 
7.4%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n25
22.3%
i24
21.4%
l19
17.0%
a9
 
8.0%
r5
 
4.5%
4
 
3.6%
d4
 
3.6%
t4
 
3.6%
o4
 
3.6%
h3
 
2.7%
Other values (7)11
9.8%

Height
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.826087
Minimum111
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:07:59.383948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile120.1
Q1122
median140
Q3170
95-th percentile170
Maximum170
Range59
Interquartile range (IQR)48

Descriptive statistics

Standard deviation21.27878195
Coefficient of variation (CV)0.1489838614
Kurtosis-1.607669316
Mean142.826087
Median Absolute Deviation (MAD)19
Skewness0.2312648733
Sum3285
Variance452.7865613
MonotonicityNot monotonic
2022-10-25T20:07:59.720390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1707
30.4%
1223
13.0%
1212
 
8.7%
1402
 
8.7%
1282
 
8.7%
1301
 
4.3%
1491
 
4.3%
1111
 
4.3%
1361
 
4.3%
1451
 
4.3%
Other values (2)2
 
8.7%
ValueCountFrequency (%)
1111
 
4.3%
1201
 
4.3%
1212
8.7%
1223
13.0%
1282
8.7%
1301
 
4.3%
1361
 
4.3%
1402
8.7%
1451
 
4.3%
1491
 
4.3%
ValueCountFrequency (%)
1707
30.4%
1601
 
4.3%
1491
 
4.3%
1451
 
4.3%
1402
 
8.7%
1361
 
4.3%
1301
 
4.3%
1282
 
8.7%
1223
13.0%
1212
 
8.7%

Weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.13043478
Minimum21
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:08:00.042392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile22.3
Q126
median35
Q370
95-th percentile70
Maximum70
Range49
Interquartile range (IQR)44

Descriptive statistics

Standard deviation20.78964853
Coefficient of variation (CV)0.471095484
Kurtosis-1.844793022
Mean44.13043478
Median Absolute Deviation (MAD)10
Skewness0.3880536102
Sum1015
Variance432.2094862
MonotonicityNot monotonic
2022-10-25T20:08:00.434053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
708
34.8%
254
17.4%
352
 
8.7%
272
 
8.7%
282
 
8.7%
381
 
4.3%
341
 
4.3%
221
 
4.3%
211
 
4.3%
601
 
4.3%
ValueCountFrequency (%)
211
 
4.3%
221
 
4.3%
254
17.4%
272
 
8.7%
282
 
8.7%
341
 
4.3%
352
 
8.7%
381
 
4.3%
601
 
4.3%
708
34.8%
ValueCountFrequency (%)
708
34.8%
601
 
4.3%
381
 
4.3%
352
 
8.7%
341
 
4.3%
282
 
8.7%
272
 
8.7%
254
17.4%
221
 
4.3%
211
 
4.3%

BMI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.34782609
Minimum14
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size312.0 B
2022-10-25T20:08:00.742133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile16
Q120.5
median23
Q323
95-th percentile23
Maximum24
Range10
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.039268687
Coefficient of variation (CV)0.1423690016
Kurtosis0.273316357
Mean21.34782609
Median Absolute Deviation (MAD)0
Skewness-1.363693143
Sum491
Variance9.23715415
MonotonicityNot monotonic
2022-10-25T20:08:01.063382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2316
69.6%
162
 
8.7%
182
 
8.7%
141
 
4.3%
241
 
4.3%
171
 
4.3%
ValueCountFrequency (%)
141
 
4.3%
162
 
8.7%
171
 
4.3%
182
 
8.7%
2316
69.6%
241
 
4.3%
ValueCountFrequency (%)
241
 
4.3%
2316
69.6%
182
 
8.7%
171
 
4.3%
162
 
8.7%
141
 
4.3%

Occupation
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
doctor
occupation
nil
business
student

Length

Max length10
Median length8
Mean length6.869565217
Min length3

Characters and Unicode

Total characters158
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdoctor
2nd rowdoctor
3rd rowdoctor
4th rowdoctor
5th rowdoctor

Common Values

ValueCountFrequency (%)
doctor9
39.1%
occupation5
21.7%
nil3
 
13.0%
business3
 
13.0%
student3
 
13.0%

Length

2022-10-25T20:08:01.421582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:01.778434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
doctor9
39.1%
occupation5
21.7%
nil3
 
13.0%
business3
 
13.0%
student3
 
13.0%

Most occurring characters

ValueCountFrequency (%)
o28
17.7%
t20
12.7%
c19
12.0%
n14
8.9%
d12
7.6%
s12
7.6%
u11
 
7.0%
i11
 
7.0%
r9
 
5.7%
e6
 
3.8%
Other values (4)16
10.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter158
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o28
17.7%
t20
12.7%
c19
12.0%
n14
8.9%
d12
7.6%
s12
7.6%
u11
 
7.0%
i11
 
7.0%
r9
 
5.7%
e6
 
3.8%
Other values (4)16
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o28
17.7%
t20
12.7%
c19
12.0%
n14
8.9%
d12
7.6%
s12
7.6%
u11
 
7.0%
i11
 
7.0%
r9
 
5.7%
e6
 
3.8%
Other values (4)16
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o28
17.7%
t20
12.7%
c19
12.0%
n14
8.9%
d12
7.6%
s12
7.6%
u11
 
7.0%
i11
 
7.0%
r9
 
5.7%
e6
 
3.8%
Other values (4)16
10.1%

Marital Status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
married
17 
unmarried

Length

Max length9
Median length7
Mean length7.52173913
Min length7

Characters and Unicode

Total characters173
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married17
73.9%
unmarried6
 
26.1%

Length

2022-10-25T20:08:02.124433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:02.460273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
married17
73.9%
unmarried6
 
26.1%

Most occurring characters

ValueCountFrequency (%)
r46
26.6%
m23
13.3%
a23
13.3%
i23
13.3%
e23
13.3%
d23
13.3%
u6
 
3.5%
n6
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter173
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r46
26.6%
m23
13.3%
a23
13.3%
i23
13.3%
e23
13.3%
d23
13.3%
u6
 
3.5%
n6
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin173
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r46
26.6%
m23
13.3%
a23
13.3%
i23
13.3%
e23
13.3%
d23
13.3%
u6
 
3.5%
n6
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r46
26.6%
m23
13.3%
a23
13.3%
i23
13.3%
e23
13.3%
d23
13.3%
u6
 
3.5%
n6
 
3.5%

Ethinicity
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
ethinicity
17 
asain

Length

Max length11
Median length11
Mean length9.434782609
Min length5

Characters and Unicode

Total characters217
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowethinicity
2nd rowethinicity
3rd rowethinicity
4th rowethinicity
5th rowethinicity

Common Values

ValueCountFrequency (%)
ethinicity 17
73.9%
asain6
 
26.1%

Length

2022-10-25T20:08:02.790837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:03.120744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ethinicity17
73.9%
asain6
 
26.1%

Most occurring characters

ValueCountFrequency (%)
i57
26.3%
t34
15.7%
n23
10.6%
e17
 
7.8%
h17
 
7.8%
c17
 
7.8%
y17
 
7.8%
17
 
7.8%
a12
 
5.5%
s6
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter200
92.2%
Space Separator17
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i57
28.5%
t34
17.0%
n23
11.5%
e17
 
8.5%
h17
 
8.5%
c17
 
8.5%
y17
 
8.5%
a12
 
6.0%
s6
 
3.0%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin200
92.2%
Common17
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i57
28.5%
t34
17.0%
n23
11.5%
e17
 
8.5%
h17
 
8.5%
c17
 
8.5%
y17
 
8.5%
a12
 
6.0%
s6
 
3.0%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i57
26.3%
t34
15.7%
n23
10.6%
e17
 
7.8%
h17
 
7.8%
c17
 
7.8%
y17
 
7.8%
17
 
7.8%
a12
 
5.5%
s6
 
2.8%

Diet
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)18.2%
Missing1
Missing (%)4.3%
Memory size312.0 B
non-veg(chicken)
11 
non-veg(readmeat)
vegeterian(cereals)
vegeterian(green leafy)
 
1

Length

Max length23
Median length21
Mean length17.22727273
Min length16

Characters and Unicode

Total characters379
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st rownon-veg(readmeat)
2nd rownon-veg(readmeat)
3rd rowvegeterian(green leafy)
4th rownon-veg(chicken)
5th rownon-veg(chicken)

Common Values

ValueCountFrequency (%)
non-veg(chicken)11
47.8%
non-veg(readmeat)5
21.7%
vegeterian(cereals)5
21.7%
vegeterian(green leafy)1
 
4.3%
(Missing)1
 
4.3%

Length

2022-10-25T20:08:03.451969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:03.820912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
non-veg(chicken11
47.8%
non-veg(readmeat5
21.7%
vegeterian(cereals5
21.7%
vegeterian(green1
 
4.3%
leafy1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e68
17.9%
n50
13.2%
c27
 
7.1%
g23
 
6.1%
(22
 
5.8%
a22
 
5.8%
)22
 
5.8%
v22
 
5.8%
i17
 
4.5%
r17
 
4.5%
Other values (12)89
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter318
83.9%
Open Punctuation22
 
5.8%
Close Punctuation22
 
5.8%
Dash Punctuation16
 
4.2%
Space Separator1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e68
21.4%
n50
15.7%
c27
 
8.5%
g23
 
7.2%
a22
 
6.9%
v22
 
6.9%
i17
 
5.3%
r17
 
5.3%
o16
 
5.0%
h11
 
3.5%
Other values (8)45
14.2%
Open Punctuation
ValueCountFrequency (%)
(22
100.0%
Close Punctuation
ValueCountFrequency (%)
)22
100.0%
Dash Punctuation
ValueCountFrequency (%)
-16
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin318
83.9%
Common61
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e68
21.4%
n50
15.7%
c27
 
8.5%
g23
 
7.2%
a22
 
6.9%
v22
 
6.9%
i17
 
5.3%
r17
 
5.3%
o16
 
5.0%
h11
 
3.5%
Other values (8)45
14.2%
Common
ValueCountFrequency (%)
(22
36.1%
)22
36.1%
-16
26.2%
1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e68
17.9%
n50
13.2%
c27
 
7.1%
g23
 
6.1%
(22
 
5.8%
a22
 
5.8%
)22
 
5.8%
v22
 
5.8%
i17
 
4.5%
r17
 
4.5%
Other values (12)89
23.5%

Allergy
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)11.1%
Missing5
Missing (%)21.7%
Memory size174.0 B
False
16 
True
(Missing)
ValueCountFrequency (%)
False16
69.6%
True2
 
8.7%
(Missing)5
 
21.7%
2022-10-25T20:08:04.177917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Alcohol intake
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
1.05
 
21.7%

Length

2022-10-25T20:08:04.488964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:04.808403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
78.3%
1.05
 
21.7%

Most occurring characters

ValueCountFrequency (%)
n18
26.1%
i18
26.1%
l18
26.1%
15
 
7.2%
.5
 
7.2%
05
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54
78.3%
Decimal Number10
 
14.5%
Other Punctuation5
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n18
33.3%
i18
33.3%
l18
33.3%
Decimal Number
ValueCountFrequency (%)
15
50.0%
05
50.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54
78.3%
Common15
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n18
33.3%
i18
33.3%
l18
33.3%
Common
ValueCountFrequency (%)
15
33.3%
.5
33.3%
05
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n18
26.1%
i18
26.1%
l18
26.1%
15
 
7.2%
.5
 
7.2%
05
 
7.2%

Smoking Cigars
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
1.05
 
21.7%

Length

2022-10-25T20:08:05.110159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:05.435399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
78.3%
1.05
 
21.7%

Most occurring characters

ValueCountFrequency (%)
n18
26.1%
i18
26.1%
l18
26.1%
15
 
7.2%
.5
 
7.2%
05
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter54
78.3%
Decimal Number10
 
14.5%
Other Punctuation5
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n18
33.3%
i18
33.3%
l18
33.3%
Decimal Number
ValueCountFrequency (%)
15
50.0%
05
50.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin54
78.3%
Common15
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n18
33.3%
i18
33.3%
l18
33.3%
Common
ValueCountFrequency (%)
15
33.3%
.5
33.3%
05
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n18
26.1%
i18
26.1%
l18
26.1%
15
 
7.2%
.5
 
7.2%
05
 
7.2%

Addiction(Others if any)
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
16 
no
1.0

Length

Max length3
Median length3
Mean length2.782608696
Min length2

Characters and Unicode

Total characters64
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil16
69.6%
no5
 
21.7%
1.02
 
8.7%

Length

2022-10-25T20:08:05.752318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:06.088824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil16
69.6%
no5
 
21.7%
1.02
 
8.7%

Most occurring characters

ValueCountFrequency (%)
n21
32.8%
i16
25.0%
l16
25.0%
o5
 
7.8%
12
 
3.1%
.2
 
3.1%
02
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58
90.6%
Decimal Number4
 
6.2%
Other Punctuation2
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n21
36.2%
i16
27.6%
l16
27.6%
o5
 
8.6%
Decimal Number
ValueCountFrequency (%)
12
50.0%
02
50.0%
Other Punctuation
ValueCountFrequency (%)
.2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58
90.6%
Common6
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n21
36.2%
i16
27.6%
l16
27.6%
o5
 
8.6%
Common
ValueCountFrequency (%)
12
33.3%
.2
33.3%
02
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII64
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n21
32.8%
i16
25.0%
l16
25.0%
o5
 
7.8%
12
 
3.1%
.2
 
3.1%
02
 
3.1%

Preffered cooking oil
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
coconut oil
mustard oil
rice bran oil
sunflower oil,mustard oil
Other values (6)

Length

Max length49
Median length25
Mean length11.56521739
Min length3

Characters and Unicode

Total characters266
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)30.4%

Sample

1st rowsunflower oil,mustard oil
2nd rownil
3rd rowmustard oil
4th rowcoconut oil ,olive oil and sunflower oil (rarely)
5th rowrice bran oil

Common Values

ValueCountFrequency (%)
nil6
26.1%
coconut oil5
21.7%
mustard oil3
13.0%
rice bran oil 2
 
8.7%
sunflower oil,mustard oil1
 
4.3%
mustard oil 1
 
4.3%
coconut oil ,olive oil and sunflower oil (rarely)1
 
4.3%
coconut oil 1
 
4.3%
rice bran oil1
 
4.3%
refined oil1
 
4.3%

Length

2022-10-25T20:08:06.418912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oil19
38.0%
coconut7
 
14.0%
nil6
 
12.0%
mustard4
 
8.0%
rice3
 
6.0%
bran3
 
6.0%
sunflower2
 
4.0%
oil,mustard1
 
2.0%
olive1
 
2.0%
and1
 
2.0%
Other values (3)3
 
6.0%

Most occurring characters

ValueCountFrequency (%)
o37
13.9%
31
11.7%
i31
11.7%
l30
11.3%
n21
7.9%
c17
 
6.4%
r16
 
6.0%
u15
 
5.6%
t13
 
4.9%
a11
 
4.1%
Other values (13)44
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter231
86.8%
Space Separator31
 
11.7%
Other Punctuation2
 
0.8%
Open Punctuation1
 
0.4%
Close Punctuation1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o37
16.0%
i31
13.4%
l30
13.0%
n21
9.1%
c17
7.4%
r16
6.9%
u15
6.5%
t13
 
5.6%
a11
 
4.8%
e10
 
4.3%
Other values (9)30
13.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
,2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin231
86.8%
Common35
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o37
16.0%
i31
13.4%
l30
13.0%
n21
9.1%
c17
7.4%
r16
6.9%
u15
6.5%
t13
 
5.6%
a11
 
4.8%
e10
 
4.3%
Other values (9)30
13.0%
Common
ValueCountFrequency (%)
31
88.6%
,2
 
5.7%
(1
 
2.9%
)1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o37
13.9%
31
11.7%
i31
11.7%
l30
11.3%
n21
7.9%
c17
 
6.4%
r16
 
6.0%
u15
 
5.6%
t13
 
4.9%
a11
 
4.1%
Other values (13)44
16.5%

Salt
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
craves
15 
not interested
prefers

Length

Max length14
Median length6
Mean length8.173913043
Min length6

Characters and Unicode

Total characters188
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot interested
2nd rowcraves
3rd rowcraves
4th rownot interested
5th rowcraves

Common Values

ValueCountFrequency (%)
craves15
65.2%
not interested6
 
26.1%
prefers2
 
8.7%

Length

2022-10-25T20:08:06.768798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:07.110757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
craves15
51.7%
not6
 
20.7%
interested6
 
20.7%
prefers2
 
6.9%

Most occurring characters

ValueCountFrequency (%)
e37
19.7%
r25
13.3%
s23
12.2%
t18
9.6%
c15
8.0%
a15
8.0%
v15
8.0%
n12
 
6.4%
o6
 
3.2%
6
 
3.2%
Other values (4)16
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter182
96.8%
Space Separator6
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e37
20.3%
r25
13.7%
s23
12.6%
t18
9.9%
c15
8.2%
a15
8.2%
v15
8.2%
n12
 
6.6%
o6
 
3.3%
i6
 
3.3%
Other values (3)10
 
5.5%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin182
96.8%
Common6
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e37
20.3%
r25
13.7%
s23
12.6%
t18
9.9%
c15
8.2%
a15
8.2%
v15
8.2%
n12
 
6.6%
o6
 
3.3%
i6
 
3.3%
Other values (3)10
 
5.5%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e37
19.7%
r25
13.3%
s23
12.2%
t18
9.6%
c15
8.0%
a15
8.0%
v15
8.0%
n12
 
6.4%
o6
 
3.2%
6
 
3.2%
Other values (4)16
8.5%

Sweet
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
craves
15 
not interested
prefers
 
1

Length

Max length14
Median length6
Mean length8.47826087
Min length6

Characters and Unicode

Total characters195
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rowcraves
2nd rowcraves
3rd rowcraves
4th rownot interested
5th rowcraves

Common Values

ValueCountFrequency (%)
craves15
65.2%
not interested7
30.4%
prefers1
 
4.3%

Length

2022-10-25T20:08:07.755172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:08.119564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
craves15
50.0%
not7
23.3%
interested7
23.3%
prefers1
 
3.3%

Most occurring characters

ValueCountFrequency (%)
e38
19.5%
r24
12.3%
s23
11.8%
t21
10.8%
c15
 
7.7%
a15
 
7.7%
v15
 
7.7%
n14
 
7.2%
o7
 
3.6%
7
 
3.6%
Other values (4)16
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter188
96.4%
Space Separator7
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e38
20.2%
r24
12.8%
s23
12.2%
t21
11.2%
c15
 
8.0%
a15
 
8.0%
v15
 
8.0%
n14
 
7.4%
o7
 
3.7%
i7
 
3.7%
Other values (3)9
 
4.8%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin188
96.4%
Common7
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e38
20.2%
r24
12.8%
s23
12.2%
t21
11.2%
c15
 
8.0%
a15
 
8.0%
v15
 
8.0%
n14
 
7.4%
o7
 
3.7%
i7
 
3.7%
Other values (3)9
 
4.8%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e38
19.5%
r24
12.3%
s23
11.8%
t21
10.8%
c15
 
7.7%
a15
 
7.7%
v15
 
7.7%
n14
 
7.2%
o7
 
3.6%
7
 
3.6%
Other values (4)16
8.2%

Blood pressure
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
15 
low

Length

Max length6
Median length6
Mean length4.956521739
Min length3

Characters and Unicode

Total characters114
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal15
65.2%
low8
34.8%

Length

2022-10-25T20:08:08.453263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:08.783853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal15
65.2%
low8
34.8%

Most occurring characters

ValueCountFrequency (%)
o23
20.2%
l23
20.2%
n15
13.2%
r15
13.2%
m15
13.2%
a15
13.2%
w8
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter114
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
20.2%
l23
20.2%
n15
13.2%
r15
13.2%
m15
13.2%
a15
13.2%
w8
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
20.2%
l23
20.2%
n15
13.2%
r15
13.2%
m15
13.2%
a15
13.2%
w8
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
20.2%
l23
20.2%
n15
13.2%
r15
13.2%
m15
13.2%
a15
13.2%
w8
 
7.0%

Hb
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
12 
low
11 

Length

Max length6
Median length6
Mean length4.565217391
Min length3

Characters and Unicode

Total characters105
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal12
52.2%
low11
47.8%

Length

2022-10-25T20:08:09.098824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:09.425650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal12
52.2%
low11
47.8%

Most occurring characters

ValueCountFrequency (%)
o23
21.9%
l23
21.9%
n12
11.4%
r12
11.4%
m12
11.4%
a12
11.4%
w11
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter105
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
21.9%
l23
21.9%
n12
11.4%
r12
11.4%
m12
11.4%
a12
11.4%
w11
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin105
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
21.9%
l23
21.9%
n12
11.4%
r12
11.4%
m12
11.4%
a12
11.4%
w11
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
21.9%
l23
21.9%
n12
11.4%
r12
11.4%
m12
11.4%
a12
11.4%
w11
10.5%

ESR
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
13 
low
high
 
1

Length

Max length6
Median length6
Mean length4.739130435
Min length3

Characters and Unicode

Total characters109
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Length

2022-10-25T20:08:09.746739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:10.078324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter109
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Fasting blood sugar
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
15 
low
high
 
1

Length

Max length6
Median length6
Mean length5
Min length3

Characters and Unicode

Total characters115
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal15
65.2%
low7
30.4%
high1
 
4.3%

Length

2022-10-25T20:08:10.402081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:10.740139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal15
65.2%
low7
30.4%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter115
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

HbA1C
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low
high
 
1

Length

Max length6
Median length6
Mean length4.869565217
Min length3

Characters and Unicode

Total characters112
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low8
34.8%
high1
 
4.3%

Length

2022-10-25T20:08:11.066315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:11.404566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low8
34.8%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter112
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Total Cholesterol
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
15 
low
high
 
1

Length

Max length6
Median length6
Mean length5
Min length3

Characters and Unicode

Total characters115
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal15
65.2%
low7
30.4%
high1
 
4.3%

Length

2022-10-25T20:08:11.738488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:12.071244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal15
65.2%
low7
30.4%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter115
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
19.1%
l22
19.1%
n15
13.0%
r15
13.0%
m15
13.0%
a15
13.0%
w7
 
6.1%
h2
 
1.7%
i1
 
0.9%
g1
 
0.9%

HDL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
13 
low
10 

Length

Max length6
Median length6
Mean length4.695652174
Min length3

Characters and Unicode

Total characters108
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal13
56.5%
low10
43.5%

Length

2022-10-25T20:08:12.389999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:12.720557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal13
56.5%
low10
43.5%

Most occurring characters

ValueCountFrequency (%)
o23
21.3%
l23
21.3%
n13
12.0%
r13
12.0%
m13
12.0%
a13
12.0%
w10
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
21.3%
l23
21.3%
n13
12.0%
r13
12.0%
m13
12.0%
a13
12.0%
w10
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
21.3%
l23
21.3%
n13
12.0%
r13
12.0%
m13
12.0%
a13
12.0%
w10
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
21.3%
l23
21.3%
n13
12.0%
r13
12.0%
m13
12.0%
a13
12.0%
w10
9.3%

LDL
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
13 
low
high

Length

Max length6
Median length6
Mean length4.782608696
Min length3

Characters and Unicode

Total characters110
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal13
56.5%
low8
34.8%
high2
 
8.7%

Length

2022-10-25T20:08:13.040291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:13.387743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal13
56.5%
low8
34.8%
high2
 
8.7%

Most occurring characters

ValueCountFrequency (%)
o21
19.1%
l21
19.1%
n13
11.8%
r13
11.8%
m13
11.8%
a13
11.8%
w8
 
7.3%
h4
 
3.6%
i2
 
1.8%
g2
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter110
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o21
19.1%
l21
19.1%
n13
11.8%
r13
11.8%
m13
11.8%
a13
11.8%
w8
 
7.3%
h4
 
3.6%
i2
 
1.8%
g2
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Latin110
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o21
19.1%
l21
19.1%
n13
11.8%
r13
11.8%
m13
11.8%
a13
11.8%
w8
 
7.3%
h4
 
3.6%
i2
 
1.8%
g2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o21
19.1%
l21
19.1%
n13
11.8%
r13
11.8%
m13
11.8%
a13
11.8%
w8
 
7.3%
h4
 
3.6%
i2
 
1.8%
g2
 
1.8%

Triglyceride
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
13 
low
high
 
1

Length

Max length6
Median length6
Mean length4.739130435
Min length3

Characters and Unicode

Total characters109
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Length

2022-10-25T20:08:13.718085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:14.053620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter109
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

TSH
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low
high
 
1

Length

Max length6
Median length6
Mean length4.869565217
Min length3

Characters and Unicode

Total characters112
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low8
34.8%
high1
 
4.3%

Length

2022-10-25T20:08:14.470789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:14.815993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low8
34.8%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter112
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin112
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
19.6%
l22
19.6%
n14
12.5%
r14
12.5%
m14
12.5%
a14
12.5%
w8
 
7.1%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Uric Acid
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low

Length

Max length6
Median length6
Mean length4.826086957
Min length3

Characters and Unicode

Total characters111
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Length

2022-10-25T20:08:15.137665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:15.487919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Most occurring characters

ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin111
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Urea
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low

Length

Max length6
Median length6
Mean length4.826086957
Min length3

Characters and Unicode

Total characters111
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Length

2022-10-25T20:08:15.937720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:16.331870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Most occurring characters

ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin111
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Creatinine
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low

Length

Max length6
Median length6
Mean length4.826086957
Min length3

Characters and Unicode

Total characters111
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Length

2022-10-25T20:08:16.662635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:17.002421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Most occurring characters

ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin111
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

SGOT
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
14 
low

Length

Max length6
Median length6
Mean length4.826086957
Min length3

Characters and Unicode

Total characters111
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rowlow
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Length

2022-10-25T20:08:17.322863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:17.662810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal14
60.9%
low9
39.1%

Most occurring characters

ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter111
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin111
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o23
20.7%
l23
20.7%
n14
12.6%
r14
12.6%
m14
12.6%
a14
12.6%
w9
 
8.1%

SGPT
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
normal
13 
low
high
 
1

Length

Max length6
Median length6
Mean length4.739130435
Min length3

Characters and Unicode

Total characters109
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rowhigh
3rd rowlow
4th rowlow
5th rowlow

Common Values

ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Length

2022-10-25T20:08:18.284780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:18.639934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal13
56.5%
low9
39.1%
high1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter109
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin109
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o22
20.2%
l22
20.2%
n13
11.9%
r13
11.9%
m13
11.9%
a13
11.9%
w9
8.3%
h2
 
1.8%
i1
 
0.9%
g1
 
0.9%

Prescritpion
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)10.0%
Missing13
Missing (%)56.5%
Memory size174.0 B
False
10 
(Missing)
13 
ValueCountFrequency (%)
False10
43.5%
(Missing)13
56.5%
2022-10-25T20:08:18.952038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Lab-Report
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)10.0%
Missing13
Missing (%)56.5%
Memory size174.0 B
False
10 
(Missing)
13 
ValueCountFrequency (%)
False10
43.5%
(Missing)13
56.5%
2022-10-25T20:08:19.252904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

X-ray
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)10.0%
Missing13
Missing (%)56.5%
Memory size174.0 B
False
10 
(Missing)
13 
ValueCountFrequency (%)
False10
43.5%
(Missing)13
56.5%
2022-10-25T20:08:19.555145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Skin allergies
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
17 
False
ValueCountFrequency (%)
True17
73.9%
False6
 
26.1%
2022-10-25T20:08:19.859407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
5.0
15 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row1.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.015
65.2%
1.08
34.8%

Length

2022-10-25T20:08:20.162078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:20.481955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
5.015
65.2%
1.08
34.8%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
515
21.7%
18
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
515
32.6%
18
 
17.4%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
515
21.7%
18
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
515
21.7%
18
 
11.6%
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.023
100.0%

Length

2022-10-25T20:08:20.778868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:21.094830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.023
100.0%

Most occurring characters

ValueCountFrequency (%)
123
33.3%
.23
33.3%
023
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
123
50.0%
023
50.0%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
123
33.3%
.23
33.3%
023
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
33.3%
.23
33.3%
023
33.3%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
16 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row5.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.016
69.6%
5.07
30.4%

Length

2022-10-25T20:08:21.395483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:21.720609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.016
69.6%
5.07
30.4%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
116
23.2%
57
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
116
34.8%
57
 
15.2%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
116
23.2%
57
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
116
23.2%
57
 
10.1%
Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
3.0
1.0
5.0
2.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row3.0
2nd row1.0
3rd row5.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.09
39.1%
1.08
34.8%
5.04
17.4%
2.01
 
4.3%
4.01
 
4.3%

Length

2022-10-25T20:08:22.024598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:22.394102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3.09
39.1%
1.08
34.8%
5.04
17.4%
2.01
 
4.3%
4.01
 
4.3%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
39
 
13.0%
18
 
11.6%
54
 
5.8%
21
 
1.4%
41
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
39
 
19.6%
18
 
17.4%
54
 
8.7%
21
 
2.2%
41
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
39
 
13.0%
18
 
11.6%
54
 
5.8%
21
 
1.4%
41
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
39
 
13.0%
18
 
11.6%
54
 
5.8%
21
 
1.4%
41
 
1.4%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
17 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row5.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.017
73.9%
5.06
 
26.1%

Length

2022-10-25T20:08:22.795376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:23.121122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.017
73.9%
5.06
 
26.1%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
117
24.6%
56
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
117
37.0%
56
 
13.0%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
117
24.6%
56
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
117
24.6%
56
 
8.7%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
19 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.019
82.6%
5.04
 
17.4%

Length

2022-10-25T20:08:23.442130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:23.775882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.019
82.6%
5.04
 
17.4%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
119
27.5%
54
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
119
41.3%
54
 
8.7%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
119
27.5%
54
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
119
27.5%
54
 
5.8%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
14 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row1.0
5th row5.0

Common Values

ValueCountFrequency (%)
1.014
60.9%
5.09
39.1%

Length

2022-10-25T20:08:24.084598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:24.408782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.014
60.9%
5.09
39.1%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
114
20.3%
59
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
114
30.4%
59
 
19.6%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
114
20.3%
59
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
114
20.3%
59
 
13.0%
Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
1.0
18 
2.0
5.0
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.018
78.3%
2.02
 
8.7%
5.02
 
8.7%
3.01
 
4.3%

Length

2022-10-25T20:08:24.711608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:25.051938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.018
78.3%
2.02
 
8.7%
5.02
 
8.7%
3.01
 
4.3%

Most occurring characters

ValueCountFrequency (%)
.23
33.3%
023
33.3%
118
26.1%
22
 
2.9%
52
 
2.9%
31
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023
50.0%
118
39.1%
22
 
4.3%
52
 
4.3%
31
 
2.2%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.23
33.3%
023
33.3%
118
26.1%
22
 
2.9%
52
 
2.9%
31
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.23
33.3%
023
33.3%
118
26.1%
22
 
2.9%
52
 
2.9%
31
 
1.4%
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
0.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.023
100.0%

Length

2022-10-25T20:08:25.362896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:25.697233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.023
100.0%

Most occurring characters

ValueCountFrequency (%)
046
66.7%
.23
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number46
66.7%
Other Punctuation23
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
046
100.0%
Other Punctuation
ValueCountFrequency (%)
.23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
046
66.7%
.23
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII69
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
046
66.7%
.23
33.3%

2. How often do you skip your meals?
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
not at all
19 
frequently

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters230
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot at all
2nd rownot at all
3rd rowfrequently
4th rowfrequently
5th rownot at all

Common Values

ValueCountFrequency (%)
not at all19
82.6%
frequently4
 
17.4%

Length

2022-10-25T20:08:25.997840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:26.326130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
not19
31.1%
at19
31.1%
all19
31.1%
frequently4
 
6.6%

Most occurring characters

ValueCountFrequency (%)
t42
18.3%
l42
18.3%
38
16.5%
a38
16.5%
n23
10.0%
o19
8.3%
e8
 
3.5%
f4
 
1.7%
r4
 
1.7%
q4
 
1.7%
Other values (2)8
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter192
83.5%
Space Separator38
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t42
21.9%
l42
21.9%
a38
19.8%
n23
12.0%
o19
9.9%
e8
 
4.2%
f4
 
2.1%
r4
 
2.1%
q4
 
2.1%
u4
 
2.1%
Space Separator
ValueCountFrequency (%)
38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin192
83.5%
Common38
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t42
21.9%
l42
21.9%
a38
19.8%
n23
12.0%
o19
9.9%
e8
 
4.2%
f4
 
2.1%
r4
 
2.1%
q4
 
2.1%
u4
 
2.1%
Common
ValueCountFrequency (%)
38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t42
18.3%
l42
18.3%
38
16.5%
a38
16.5%
n23
10.0%
o19
8.3%
e8
 
3.5%
f4
 
1.7%
r4
 
1.7%
q4
 
1.7%
Other values (2)8
 
3.5%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
not at all
18 
frequently

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters230
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot at all
2nd rownot at all
3rd rownot at all
4th rowfrequently
5th rowfrequently

Common Values

ValueCountFrequency (%)
not at all18
78.3%
frequently5
 
21.7%

Length

2022-10-25T20:08:26.632007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:26.958275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
not18
30.5%
at18
30.5%
all18
30.5%
frequently5
 
8.5%

Most occurring characters

ValueCountFrequency (%)
t41
17.8%
l41
17.8%
36
15.7%
a36
15.7%
n23
10.0%
o18
7.8%
e10
 
4.3%
f5
 
2.2%
r5
 
2.2%
q5
 
2.2%
Other values (2)10
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter194
84.3%
Space Separator36
 
15.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t41
21.1%
l41
21.1%
a36
18.6%
n23
11.9%
o18
9.3%
e10
 
5.2%
f5
 
2.6%
r5
 
2.6%
q5
 
2.6%
u5
 
2.6%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin194
84.3%
Common36
 
15.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t41
21.1%
l41
21.1%
a36
18.6%
n23
11.9%
o18
9.3%
e10
 
5.2%
f5
 
2.6%
r5
 
2.6%
q5
 
2.6%
u5
 
2.6%
Common
ValueCountFrequency (%)
36
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t41
17.8%
l41
17.8%
36
15.7%
a36
15.7%
n23
10.0%
o18
7.8%
e10
 
4.3%
f5
 
2.2%
r5
 
2.2%
q5
 
2.2%
Other values (2)10
 
4.3%

4. How much time do you sleep in a day
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)21.1%
Missing4
Missing (%)17.4%
Memory size312.0 B
c. 6-8 hours
d. more than 8 hours
a. less than 5 hours
b. 5 -6 hours

Length

Max length20
Median length13
Mean length15.47368421
Min length12

Characters and Unicode

Total characters294
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd. more than 8 hours
2nd rowd. more than 8 hours
3rd rowc. 6-8 hours
4th rowa. less than 5 hours
5th rowa. less than 5 hours

Common Values

ValueCountFrequency (%)
c. 6-8 hours9
39.1%
d. more than 8 hours5
21.7%
a. less than 5 hours3
 
13.0%
b. 5 -6 hours2
 
8.7%
(Missing)4
17.4%

Length

2022-10-25T20:08:27.279324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:27.651553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
hours19
25.3%
c9
12.0%
6-89
12.0%
than8
10.7%
d5
 
6.7%
more5
 
6.7%
85
 
6.7%
55
 
6.7%
a3
 
4.0%
less3
 
4.0%
Other values (2)4
 
5.3%

Most occurring characters

ValueCountFrequency (%)
56
19.0%
h27
9.2%
s25
 
8.5%
o24
 
8.2%
r24
 
8.2%
u19
 
6.5%
.19
 
6.5%
814
 
4.8%
a11
 
3.7%
611
 
3.7%
Other values (10)64
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter178
60.5%
Space Separator56
 
19.0%
Decimal Number30
 
10.2%
Other Punctuation19
 
6.5%
Dash Punctuation11
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h27
15.2%
s25
14.0%
o24
13.5%
r24
13.5%
u19
10.7%
a11
6.2%
c9
 
5.1%
e8
 
4.5%
t8
 
4.5%
n8
 
4.5%
Other values (4)15
8.4%
Decimal Number
ValueCountFrequency (%)
814
46.7%
611
36.7%
55
 
16.7%
Space Separator
ValueCountFrequency (%)
56
100.0%
Other Punctuation
ValueCountFrequency (%)
.19
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin178
60.5%
Common116
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
h27
15.2%
s25
14.0%
o24
13.5%
r24
13.5%
u19
10.7%
a11
6.2%
c9
 
5.1%
e8
 
4.5%
t8
 
4.5%
n8
 
4.5%
Other values (4)15
8.4%
Common
ValueCountFrequency (%)
56
48.3%
.19
 
16.4%
814
 
12.1%
611
 
9.5%
-11
 
9.5%
55
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
19.0%
h27
9.2%
s25
 
8.5%
o24
 
8.2%
r24
 
8.2%
u19
 
6.5%
.19
 
6.5%
814
 
4.8%
a11
 
3.7%
611
 
3.7%
Other values (10)64
21.8%
Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
frequently
11 
not at all
10 
all days of the week

Length

Max length20
Median length10
Mean length10.86956522
Min length10

Characters and Unicode

Total characters250
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot at all
2nd rowfrequently
3rd rowfrequently
4th rowfrequently
5th rownot at all

Common Values

ValueCountFrequency (%)
frequently11
47.8%
not at all10
43.5%
all days of the week2
 
8.7%

Length

2022-10-25T20:08:27.999545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:28.340976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
all12
23.5%
frequently11
21.6%
not10
19.6%
at10
19.6%
days2
 
3.9%
of2
 
3.9%
the2
 
3.9%
week2
 
3.9%

Most occurring characters

ValueCountFrequency (%)
l35
14.0%
t33
13.2%
e28
11.2%
28
11.2%
a24
9.6%
n21
8.4%
f13
 
5.2%
y13
 
5.2%
o12
 
4.8%
r11
 
4.4%
Other values (7)32
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter222
88.8%
Space Separator28
 
11.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l35
15.8%
t33
14.9%
e28
12.6%
a24
10.8%
n21
9.5%
f13
 
5.9%
y13
 
5.9%
o12
 
5.4%
r11
 
5.0%
u11
 
5.0%
Other values (6)21
9.5%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin222
88.8%
Common28
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
l35
15.8%
t33
14.9%
e28
12.6%
a24
10.8%
n21
9.5%
f13
 
5.9%
y13
 
5.9%
o12
 
5.4%
r11
 
5.0%
u11
 
5.0%
Other values (6)21
9.5%
Common
ValueCountFrequency (%)
28
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l35
14.0%
t33
13.2%
e28
11.2%
28
11.2%
a24
9.6%
n21
8.4%
f13
 
5.2%
y13
 
5.2%
o12
 
4.8%
r11
 
4.4%
Other values (7)32
12.8%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
not at all
17 
frequently

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters230
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot at all
2nd rownot at all
3rd rownot at all
4th rownot at all
5th rowfrequently

Common Values

ValueCountFrequency (%)
not at all17
73.9%
frequently6
 
26.1%

Length

2022-10-25T20:08:28.957489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:29.280742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
not17
29.8%
at17
29.8%
all17
29.8%
frequently6
 
10.5%

Most occurring characters

ValueCountFrequency (%)
t40
17.4%
l40
17.4%
34
14.8%
a34
14.8%
n23
10.0%
o17
7.4%
e12
 
5.2%
f6
 
2.6%
r6
 
2.6%
q6
 
2.6%
Other values (2)12
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter196
85.2%
Space Separator34
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t40
20.4%
l40
20.4%
a34
17.3%
n23
11.7%
o17
8.7%
e12
 
6.1%
f6
 
3.1%
r6
 
3.1%
q6
 
3.1%
u6
 
3.1%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin196
85.2%
Common34
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t40
20.4%
l40
20.4%
a34
17.3%
n23
11.7%
o17
8.7%
e12
 
6.1%
f6
 
3.1%
r6
 
3.1%
q6
 
3.1%
u6
 
3.1%
Common
ValueCountFrequency (%)
34
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t40
17.4%
l40
17.4%
34
14.8%
a34
14.8%
n23
10.0%
o17
7.4%
e12
 
5.2%
f6
 
2.6%
r6
 
2.6%
q6
 
2.6%
Other values (2)12
 
5.2%
Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
frequently
16 
not at all
all days of the week
 
1

Length

Max length20
Median length10
Mean length10.43478261
Min length10

Characters and Unicode

Total characters240
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownot at all
2nd rownot at all
3rd rowfrequently
4th rownot at all
5th rowfrequently

Common Values

ValueCountFrequency (%)
frequently16
69.6%
not at all6
 
26.1%
all days of the week1
 
4.3%

Length

2022-10-25T20:08:29.589353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:29.921807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
frequently16
41.0%
all7
17.9%
not6
 
15.4%
at6
 
15.4%
days1
 
2.6%
of1
 
2.6%
the1
 
2.6%
week1
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e35
14.6%
l30
12.5%
t29
12.1%
n22
9.2%
f17
7.1%
y17
7.1%
u16
6.7%
q16
6.7%
r16
6.7%
16
6.7%
Other values (7)26
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter224
93.3%
Space Separator16
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e35
15.6%
l30
13.4%
t29
12.9%
n22
9.8%
f17
7.6%
y17
7.6%
u16
7.1%
q16
7.1%
r16
7.1%
a14
 
6.2%
Other values (6)12
 
5.4%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224
93.3%
Common16
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e35
15.6%
l30
13.4%
t29
12.9%
n22
9.8%
f17
7.6%
y17
7.6%
u16
7.1%
q16
7.1%
r16
7.1%
a14
 
6.2%
Other values (6)12
 
5.4%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e35
14.6%
l30
12.5%
t29
12.1%
n22
9.2%
f17
7.1%
y17
7.1%
u16
6.7%
q16
6.7%
r16
6.7%
16
6.7%
Other values (7)26
10.8%
Distinct3
Distinct (%)20.0%
Missing8
Missing (%)34.8%
Memory size312.0 B
i feel i have normal sex drive
13 
i have increased sex drive
 
1
no sex drive these days
 
1

Length

Max length30
Median length30
Mean length29.26666667
Min length23

Characters and Unicode

Total characters439
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)13.3%

Sample

1st rowi feel i have normal sex drive
2nd rowi feel i have normal sex drive
3rd rowi feel i have normal sex drive
4th rowi feel i have normal sex drive
5th rowi feel i have normal sex drive

Common Values

ValueCountFrequency (%)
i feel i have normal sex drive13
56.5%
i have increased sex drive1
 
4.3%
no sex drive these days1
 
4.3%
(Missing)8
34.8%

Length

2022-10-25T20:08:30.254243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:30.606899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
i27
26.7%
sex15
14.9%
drive15
14.9%
have14
13.9%
feel13
12.9%
normal13
12.9%
increased1
 
1.0%
no1
 
1.0%
these1
 
1.0%
days1
 
1.0%

Most occurring characters

ValueCountFrequency (%)
86
19.6%
e74
16.9%
i43
9.8%
a29
 
6.6%
v29
 
6.6%
r29
 
6.6%
l26
 
5.9%
s18
 
4.1%
d17
 
3.9%
h15
 
3.4%
Other values (8)73
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter353
80.4%
Space Separator86
 
19.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e74
21.0%
i43
12.2%
a29
 
8.2%
v29
 
8.2%
r29
 
8.2%
l26
 
7.4%
s18
 
5.1%
d17
 
4.8%
h15
 
4.2%
n15
 
4.2%
Other values (7)58
16.4%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin353
80.4%
Common86
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e74
21.0%
i43
12.2%
a29
 
8.2%
v29
 
8.2%
r29
 
8.2%
l26
 
7.4%
s18
 
5.1%
d17
 
4.8%
h15
 
4.2%
n15
 
4.2%
Other values (7)58
16.4%
Common
ValueCountFrequency (%)
86
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII439
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
19.6%
e74
16.9%
i43
9.8%
a29
 
6.6%
v29
 
6.6%
r29
 
6.6%
l26
 
5.9%
s18
 
4.1%
d17
 
3.9%
h15
 
3.4%
Other values (8)73
16.6%

8. How often do you exercise?
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
not at all
13 
2-3 hours a week
more than 5 hours a week
once in a while

Length

Max length24
Median length10
Mean length13.56521739
Min length10

Characters and Unicode

Total characters312
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot at all
2nd rowmore than 5 hours a week
3rd rownot at all
4th rownot at all
5th rownot at all

Common Values

ValueCountFrequency (%)
not at all13
56.5%
2-3 hours a week5
 
21.7%
more than 5 hours a week3
 
13.0%
once in a while2
 
8.7%

Length

2022-10-25T20:08:30.934345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:31.287066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
not13
15.3%
at13
15.3%
all13
15.3%
a10
11.8%
hours8
9.4%
week8
9.4%
2-35
 
5.9%
more3
 
3.5%
than3
 
3.5%
53
 
3.5%
Other values (3)6
7.1%

Most occurring characters

ValueCountFrequency (%)
62
19.9%
a39
12.5%
t29
9.3%
l28
9.0%
o26
8.3%
e23
 
7.4%
n20
 
6.4%
h13
 
4.2%
r11
 
3.5%
w10
 
3.2%
Other values (10)51
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter232
74.4%
Space Separator62
 
19.9%
Decimal Number13
 
4.2%
Dash Punctuation5
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a39
16.8%
t29
12.5%
l28
12.1%
o26
11.2%
e23
9.9%
n20
8.6%
h13
 
5.6%
r11
 
4.7%
w10
 
4.3%
s8
 
3.4%
Other values (5)25
10.8%
Decimal Number
ValueCountFrequency (%)
35
38.5%
25
38.5%
53
23.1%
Space Separator
ValueCountFrequency (%)
62
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin232
74.4%
Common80
 
25.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a39
16.8%
t29
12.5%
l28
12.1%
o26
11.2%
e23
9.9%
n20
8.6%
h13
 
5.6%
r11
 
4.7%
w10
 
4.3%
s8
 
3.4%
Other values (5)25
10.8%
Common
ValueCountFrequency (%)
62
77.5%
35
 
6.2%
-5
 
6.2%
25
 
6.2%
53
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62
19.9%
a39
12.5%
t29
9.3%
l28
9.0%
o26
8.3%
e23
 
7.4%
n20
 
6.4%
h13
 
4.2%
r11
 
3.5%
w10
 
3.2%
Other values (10)51
16.3%

9. How will you describe your skin type?
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)21.1%
Missing4
Missing (%)17.4%
Memory size312.0 B
normal
12 
dry
combination of dry and oily
 
1
oily
 
1

Length

Max length27
Median length6
Mean length6.210526316
Min length3

Characters and Unicode

Total characters118
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)10.5%

Sample

1st rownormal
2nd rownormal
3rd rowdry
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal12
52.2%
dry5
21.7%
combination of dry and oily1
 
4.3%
oily1
 
4.3%
(Missing)4
 
17.4%

Length

2022-10-25T20:08:31.625050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:31.997584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
normal12
52.2%
dry6
26.1%
oily2
 
8.7%
combination1
 
4.3%
of1
 
4.3%
and1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r18
15.3%
o17
14.4%
n15
12.7%
a14
11.9%
l14
11.9%
m13
11.0%
y8
6.8%
d7
 
5.9%
i4
 
3.4%
4
 
3.4%
Other values (4)4
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter114
96.6%
Space Separator4
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r18
15.8%
o17
14.9%
n15
13.2%
a14
12.3%
l14
12.3%
m13
11.4%
y8
7.0%
d7
 
6.1%
i4
 
3.5%
c1
 
0.9%
Other values (3)3
 
2.6%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114
96.6%
Common4
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r18
15.8%
o17
14.9%
n15
13.2%
a14
12.3%
l14
12.3%
m13
11.4%
y8
7.0%
d7
 
6.1%
i4
 
3.5%
c1
 
0.9%
Other values (3)3
 
2.6%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r18
15.3%
o17
14.4%
n15
12.7%
a14
11.9%
l14
11.9%
m13
11.0%
y8
6.8%
d7
 
5.9%
i4
 
3.4%
4
 
3.4%
Other values (4)4
 
3.4%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
22 
True
 
1
ValueCountFrequency (%)
False22
95.7%
True1
 
4.3%
2022-10-25T20:08:32.333123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
23 
ValueCountFrequency (%)
True23
100.0%
2022-10-25T20:08:32.633565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:32.948425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
16 
True
ValueCountFrequency (%)
False16
69.6%
True7
30.4%
2022-10-25T20:08:33.271818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
20 
True
ValueCountFrequency (%)
False20
87.0%
True3
 
13.0%
2022-10-25T20:08:33.593815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
True
ValueCountFrequency (%)
False19
82.6%
True4
 
17.4%
2022-10-25T20:08:33.917891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
18 
True
ValueCountFrequency (%)
False18
78.3%
True5
 
21.7%
2022-10-25T20:08:34.246750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:34.560730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:34.887281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:35.196374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
16 
True
ValueCountFrequency (%)
False16
69.6%
True7
30.4%
2022-10-25T20:08:35.513842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
23 
ValueCountFrequency (%)
False23
100.0%
2022-10-25T20:08:35.822710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
23 
ValueCountFrequency (%)
False23
100.0%
2022-10-25T20:08:36.136573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
16 
True
ValueCountFrequency (%)
False16
69.6%
True7
30.4%
2022-10-25T20:08:36.441295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
18 
True
ValueCountFrequency (%)
False18
78.3%
True5
 
21.7%
2022-10-25T20:08:36.753757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
23 
ValueCountFrequency (%)
False23
100.0%
2022-10-25T20:08:37.053349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
21 
True
 
2
ValueCountFrequency (%)
False21
91.3%
True2
 
8.7%
2022-10-25T20:08:37.462010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
True
ValueCountFrequency (%)
False19
82.6%
True4
 
17.4%
2022-10-25T20:08:37.774500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
18 
True
ValueCountFrequency (%)
False18
78.3%
True5
 
21.7%
2022-10-25T20:08:38.095003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
21 
True
 
2
ValueCountFrequency (%)
False21
91.3%
True2
 
8.7%
2022-10-25T20:08:38.407924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
12 
False
11 
ValueCountFrequency (%)
True12
52.2%
False11
47.8%
2022-10-25T20:08:38.718716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
18 
True
ValueCountFrequency (%)
False18
78.3%
True5
 
21.7%
2022-10-25T20:08:39.035772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
True
ValueCountFrequency (%)
False19
82.6%
True4
 
17.4%
2022-10-25T20:08:39.343846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
21 
True
 
2
ValueCountFrequency (%)
False21
91.3%
True2
 
8.7%
2022-10-25T20:08:39.976765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
22 
False
 
1
ValueCountFrequency (%)
True22
95.7%
False1
 
4.3%
2022-10-25T20:08:40.284150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
22 
False
 
1
ValueCountFrequency (%)
True22
95.7%
False1
 
4.3%
2022-10-25T20:08:40.585380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
22 
False
 
1
ValueCountFrequency (%)
True22
95.7%
False1
 
4.3%
2022-10-25T20:08:40.887358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
22 
False
 
1
ValueCountFrequency (%)
True22
95.7%
False1
 
4.3%
2022-10-25T20:08:41.187715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
22 
False
 
1
ValueCountFrequency (%)
True22
95.7%
False1
 
4.3%
2022-10-25T20:08:41.493499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct1
Distinct (%)100.0%
Missing22
Missing (%)95.7%
Memory size174.0 B
False
 
1
(Missing)
22 
ValueCountFrequency (%)
False1
 
4.3%
(Missing)22
95.7%
2022-10-25T20:08:41.796352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

A. Age of Menarche
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
13.0
11.0
 
1
a. age of menarche
 
1

Length

Max length18
Median length3
Mean length3.826086957
Min length3

Characters and Unicode

Total characters88
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row13.0
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
13.03
 
13.0%
11.01
 
4.3%
a. age of menarche1
 
4.3%

Length

2022-10-25T20:08:42.126160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:42.622934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
69.2%
13.03
 
11.5%
11.01
 
3.8%
a1
 
3.8%
age1
 
3.8%
of1
 
3.8%
menarche1
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n19
21.6%
i18
20.5%
l18
20.5%
15
 
5.7%
.5
 
5.7%
04
 
4.5%
e3
 
3.4%
3
 
3.4%
a3
 
3.4%
33
 
3.4%
Other values (7)7
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter68
77.3%
Decimal Number12
 
13.6%
Other Punctuation5
 
5.7%
Space Separator3
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n19
27.9%
i18
26.5%
l18
26.5%
e3
 
4.4%
a3
 
4.4%
g1
 
1.5%
o1
 
1.5%
f1
 
1.5%
m1
 
1.5%
r1
 
1.5%
Other values (2)2
 
2.9%
Decimal Number
ValueCountFrequency (%)
15
41.7%
04
33.3%
33
25.0%
Other Punctuation
ValueCountFrequency (%)
.5
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68
77.3%
Common20
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n19
27.9%
i18
26.5%
l18
26.5%
e3
 
4.4%
a3
 
4.4%
g1
 
1.5%
o1
 
1.5%
f1
 
1.5%
m1
 
1.5%
r1
 
1.5%
Other values (2)2
 
2.9%
Common
ValueCountFrequency (%)
15
25.0%
.5
25.0%
04
20.0%
3
15.0%
33
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n19
21.6%
i18
20.5%
l18
20.5%
15
 
5.7%
.5
 
5.7%
04
 
4.5%
e3
 
3.4%
3
 
3.4%
a3
 
3.4%
33
 
3.4%
Other values (7)7
 
8.0%

B. Cycle length
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
15 
normal
less than 24 days
 
1
b. cycle length
 
1

Length

Max length17
Median length3
Mean length4.913043478
Min length3

Characters and Unicode

Total characters113
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st rownormal
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil15
65.2%
normal6
 
26.1%
less than 24 days1
 
4.3%
b. cycle length1
 
4.3%

Length

2022-10-25T20:08:42.969356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:43.319475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil15
53.6%
normal6
 
21.4%
less1
 
3.6%
than1
 
3.6%
241
 
3.6%
days1
 
3.6%
b1
 
3.6%
cycle1
 
3.6%
length1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
l24
21.2%
n23
20.4%
i15
13.3%
a8
 
7.1%
o6
 
5.3%
r6
 
5.3%
m6
 
5.3%
5
 
4.4%
e3
 
2.7%
s3
 
2.7%
Other values (10)14
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter105
92.9%
Space Separator5
 
4.4%
Decimal Number2
 
1.8%
Other Punctuation1
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l24
22.9%
n23
21.9%
i15
14.3%
a8
 
7.6%
o6
 
5.7%
r6
 
5.7%
m6
 
5.7%
e3
 
2.9%
s3
 
2.9%
y2
 
1.9%
Other values (6)9
 
8.6%
Decimal Number
ValueCountFrequency (%)
41
50.0%
21
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin105
92.9%
Common8
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l24
22.9%
n23
21.9%
i15
14.3%
a8
 
7.6%
o6
 
5.7%
r6
 
5.7%
m6
 
5.7%
e3
 
2.9%
s3
 
2.9%
y2
 
1.9%
Other values (6)9
 
8.6%
Common
ValueCountFrequency (%)
5
62.5%
41
 
12.5%
21
 
12.5%
.1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l24
21.2%
n23
20.4%
i15
13.3%
a8
 
7.1%
o6
 
5.3%
r6
 
5.3%
m6
 
5.3%
5
 
4.4%
e3
 
2.7%
s3
 
2.7%
Other values (10)14
12.4%

C. Regularity
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
regular
22 
irregular
 
1

Length

Max length9
Median length7
Mean length7.086956522
Min length7

Characters and Unicode

Total characters163
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular22
95.7%
irregular1
 
4.3%

Length

2022-10-25T20:08:43.656586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:44.010241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
regular22
95.7%
irregular1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
r47
28.8%
e23
14.1%
g23
14.1%
u23
14.1%
l23
14.1%
a23
14.1%
i1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter163
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r47
28.8%
e23
14.1%
g23
14.1%
u23
14.1%
l23
14.1%
a23
14.1%
i1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin163
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r47
28.8%
e23
14.1%
g23
14.1%
u23
14.1%
l23
14.1%
a23
14.1%
i1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r47
28.8%
e23
14.1%
g23
14.1%
u23
14.1%
l23
14.1%
a23
14.1%
i1
 
0.6%

D. Days of flow
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
4.0
d. days of flow
 
1
2022-07-05 00:00:00
 
1

Length

Max length19
Median length3
Mean length4.217391304
Min length3

Characters and Unicode

Total characters97
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row4.0
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
4.03
 
13.0%
d. days of flow1
 
4.3%
2022-07-05 00:00:001
 
4.3%

Length

2022-10-25T20:08:44.516960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:44.886185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
66.7%
4.03
 
11.1%
d1
 
3.7%
days1
 
3.7%
of1
 
3.7%
flow1
 
3.7%
2022-07-051
 
3.7%
00:00:001
 
3.7%

Most occurring characters

ValueCountFrequency (%)
l19
19.6%
n18
18.6%
i18
18.6%
012
12.4%
.4
 
4.1%
4
 
4.1%
43
 
3.1%
23
 
3.1%
o2
 
2.1%
-2
 
2.1%
Other values (9)12
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter65
67.0%
Decimal Number20
 
20.6%
Other Punctuation6
 
6.2%
Space Separator4
 
4.1%
Dash Punctuation2
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l19
29.2%
n18
27.7%
i18
27.7%
o2
 
3.1%
f2
 
3.1%
d2
 
3.1%
s1
 
1.5%
w1
 
1.5%
a1
 
1.5%
y1
 
1.5%
Decimal Number
ValueCountFrequency (%)
012
60.0%
43
 
15.0%
23
 
15.0%
71
 
5.0%
51
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.4
66.7%
:2
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin65
67.0%
Common32
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l19
29.2%
n18
27.7%
i18
27.7%
o2
 
3.1%
f2
 
3.1%
d2
 
3.1%
s1
 
1.5%
w1
 
1.5%
a1
 
1.5%
y1
 
1.5%
Common
ValueCountFrequency (%)
012
37.5%
.4
 
12.5%
4
 
12.5%
43
 
9.4%
23
 
9.4%
-2
 
6.2%
:2
 
6.2%
71
 
3.1%
51
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII97
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l19
19.6%
n18
18.6%
i18
18.6%
012
12.4%
.4
 
4.1%
4
 
4.1%
43
 
3.1%
23
 
3.1%
o2
 
2.1%
-2
 
2.1%
Other values (9)12
12.4%
Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
normal
heavy
e. quantity of bleeding- heavy/normal/scanty
 
1

Length

Max length44
Median length3
Mean length5.217391304
Min length3

Characters and Unicode

Total characters120
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownormal
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
normal2
 
8.7%
heavy2
 
8.7%
e. quantity of bleeding- heavy/normal/scanty1
 
4.3%

Length

2022-10-25T20:08:45.214457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:45.563550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
66.7%
normal2
 
7.4%
heavy2
 
7.4%
e1
 
3.7%
quantity1
 
3.7%
of1
 
3.7%
bleeding1
 
3.7%
heavy/normal/scanty1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
n24
20.0%
l22
18.3%
i20
16.7%
a8
 
6.7%
e6
 
5.0%
y5
 
4.2%
4
 
3.3%
o4
 
3.3%
h3
 
2.5%
m3
 
2.5%
Other values (14)21
17.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter112
93.3%
Space Separator4
 
3.3%
Other Punctuation3
 
2.5%
Dash Punctuation1
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n24
21.4%
l22
19.6%
i20
17.9%
a8
 
7.1%
e6
 
5.4%
y5
 
4.5%
o4
 
3.6%
h3
 
2.7%
m3
 
2.7%
t3
 
2.7%
Other values (10)14
12.5%
Other Punctuation
ValueCountFrequency (%)
/2
66.7%
.1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin112
93.3%
Common8
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n24
21.4%
l22
19.6%
i20
17.9%
a8
 
7.1%
e6
 
5.4%
y5
 
4.5%
o4
 
3.6%
h3
 
2.7%
m3
 
2.7%
t3
 
2.7%
Other values (10)14
12.5%
Common
ValueCountFrequency (%)
4
50.0%
/2
25.0%
.1
 
12.5%
-1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n24
20.0%
l22
18.3%
i20
16.7%
a8
 
6.7%
e6
 
5.0%
y5
 
4.2%
4
 
3.3%
o4
 
3.3%
h3
 
2.5%
m3
 
2.5%
Other values (14)21
17.5%

F. Clots
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
16 
True
ValueCountFrequency (%)
False16
69.6%
True7
30.4%
2022-10-25T20:08:45.897303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

G. Abnormal discharges
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
20 
True
ValueCountFrequency (%)
False20
87.0%
True3
 
13.0%
2022-10-25T20:08:46.208999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
18 
yes
pain,cramps,depressed
 
1
pain,cramps,
 
1
h. associated symptoms- pains/pms/cramps/low back pain/ mood swings/depressed
 
1

Length

Max length77
Median length3
Mean length7.391304348
Min length3

Characters and Unicode

Total characters170
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st rowpain,cramps,depressed
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil18
78.3%
yes2
 
8.7%
pain,cramps,depressed1
 
4.3%
pain,cramps,1
 
4.3%
h. associated symptoms- pains/pms/cramps/low back pain/ mood swings/depressed1
 
4.3%

Length

2022-10-25T20:08:46.539572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:46.899353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil18
60.0%
yes2
 
6.7%
pain,cramps,depressed1
 
3.3%
pain,cramps1
 
3.3%
h1
 
3.3%
associated1
 
3.3%
symptoms1
 
3.3%
pains/pms/cramps/low1
 
3.3%
back1
 
3.3%
pain1
 
3.3%
Other values (2)2
 
6.7%

Most occurring characters

ValueCountFrequency (%)
i24
14.1%
n23
13.5%
l19
11.2%
s17
10.0%
p11
 
6.5%
a10
 
5.9%
e9
 
5.3%
m7
 
4.1%
7
 
4.1%
d6
 
3.5%
Other values (14)37
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter152
89.4%
Other Punctuation10
 
5.9%
Space Separator7
 
4.1%
Dash Punctuation1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i24
15.8%
n23
15.1%
l19
12.5%
s17
11.2%
p11
7.2%
a10
6.6%
e9
 
5.9%
m7
 
4.6%
d6
 
3.9%
r5
 
3.3%
Other values (9)21
13.8%
Other Punctuation
ValueCountFrequency (%)
/5
50.0%
,4
40.0%
.1
 
10.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin152
89.4%
Common18
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i24
15.8%
n23
15.1%
l19
12.5%
s17
11.2%
p11
7.2%
a10
6.6%
e9
 
5.9%
m7
 
4.6%
d6
 
3.9%
r5
 
3.3%
Other values (9)21
13.8%
Common
ValueCountFrequency (%)
7
38.9%
/5
27.8%
,4
22.2%
.1
 
5.6%
-1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i24
14.1%
n23
13.5%
l19
11.2%
s17
10.0%
p11
 
6.5%
a10
 
5.9%
e9
 
5.3%
m7
 
4.1%
7
 
4.1%
d6
 
3.5%
Other values (14)37
21.8%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
13 
True
10 
ValueCountFrequency (%)
False13
56.5%
True10
43.5%
2022-10-25T20:08:47.236613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
13 
True
10 
ValueCountFrequency (%)
False13
56.5%
True10
43.5%
2022-10-25T20:08:47.550727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
16 
False
ValueCountFrequency (%)
True16
69.6%
False7
30.4%
2022-10-25T20:08:47.868158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

MENOPAUSAL FEMALES ONLY
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing23
Missing (%)100.0%
Memory size312.0 B

1. Age of Menopause
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing23
Missing (%)100.0%
Memory size312.0 B
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:48.182844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
13 
True
10 
ValueCountFrequency (%)
False13
56.5%
True10
43.5%
2022-10-25T20:08:48.493264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
21 
True
 
2
ValueCountFrequency (%)
False21
91.3%
True2
 
8.7%
2022-10-25T20:08:48.807583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
True
ValueCountFrequency (%)
False19
82.6%
True4
 
17.4%
2022-10-25T20:08:49.116494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
12 
True
11 
ValueCountFrequency (%)
False12
52.2%
True11
47.8%
2022-10-25T20:08:49.484607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
17 
False
ValueCountFrequency (%)
True17
73.9%
False6
 
26.1%
2022-10-25T20:08:49.959339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
19 
True
ValueCountFrequency (%)
False19
82.6%
True4
 
17.4%
2022-10-25T20:08:50.288585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
active
16 
sluggish
4. how will you describe yourself? sluggish/active/hyperactive
hyperactive
 
1

Length

Max length62
Median length6
Mean length13.7826087
Min length6

Characters and Unicode

Total characters317
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rowsluggish
2nd rowactive
3rd rowactive
4th rowactive
5th rowactive

Common Values

ValueCountFrequency (%)
active16
69.6%
sluggish3
 
13.0%
4. how will you describe yourself? sluggish/active/hyperactive3
 
13.0%
hyperactive1
 
4.3%

Length

2022-10-25T20:08:50.615102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:51.287799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
active16
39.0%
sluggish3
 
7.3%
43
 
7.3%
how3
 
7.3%
will3
 
7.3%
you3
 
7.3%
describe3
 
7.3%
yourself3
 
7.3%
sluggish/active/hyperactive3
 
7.3%
hyperactive1
 
2.4%

Most occurring characters

ValueCountFrequency (%)
e36
11.4%
i35
11.0%
c26
 
8.2%
a23
 
7.3%
t23
 
7.3%
v23
 
7.3%
s18
 
5.7%
18
 
5.7%
l15
 
4.7%
h13
 
4.1%
Other values (14)87
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter284
89.6%
Space Separator18
 
5.7%
Other Punctuation12
 
3.8%
Decimal Number3
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e36
12.7%
i35
12.3%
c26
9.2%
a23
 
8.1%
t23
 
8.1%
v23
 
8.1%
s18
 
6.3%
l15
 
5.3%
h13
 
4.6%
u12
 
4.2%
Other values (9)60
21.1%
Other Punctuation
ValueCountFrequency (%)
/6
50.0%
?3
25.0%
.3
25.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Decimal Number
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin284
89.6%
Common33
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e36
12.7%
i35
12.3%
c26
9.2%
a23
 
8.1%
t23
 
8.1%
v23
 
8.1%
s18
 
6.3%
l15
 
5.3%
h13
 
4.6%
u12
 
4.2%
Other values (9)60
21.1%
Common
ValueCountFrequency (%)
18
54.5%
/6
 
18.2%
?3
 
9.1%
43
 
9.1%
.3
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e36
11.4%
i35
11.0%
c26
 
8.2%
a23
 
7.3%
t23
 
7.3%
v23
 
7.3%
s18
 
5.7%
18
 
5.7%
l15
 
4.7%
h13
 
4.1%
Other values (14)87
27.4%
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
17 
True
ValueCountFrequency (%)
False17
73.9%
True6
 
26.1%
2022-10-25T20:08:51.615589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

2. How long are you suffering?
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
22 
4 years
 
1

Length

Max length7
Median length3
Mean length3.173913043
Min length3

Characters and Unicode

Total characters73
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil22
95.7%
4 years1
 
4.3%

Length

2022-10-25T20:08:51.946942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:52.280107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil22
91.7%
41
 
4.2%
years1
 
4.2%

Most occurring characters

ValueCountFrequency (%)
n22
30.1%
i22
30.1%
l22
30.1%
41
 
1.4%
1
 
1.4%
y1
 
1.4%
e1
 
1.4%
a1
 
1.4%
r1
 
1.4%
s1
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71
97.3%
Decimal Number1
 
1.4%
Space Separator1
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n22
31.0%
i22
31.0%
l22
31.0%
y1
 
1.4%
e1
 
1.4%
a1
 
1.4%
r1
 
1.4%
s1
 
1.4%
Decimal Number
ValueCountFrequency (%)
41
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin71
97.3%
Common2
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n22
31.0%
i22
31.0%
l22
31.0%
y1
 
1.4%
e1
 
1.4%
a1
 
1.4%
r1
 
1.4%
s1
 
1.4%
Common
ValueCountFrequency (%)
41
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII73
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n22
30.1%
i22
30.1%
l22
30.1%
41
 
1.4%
1
 
1.4%
y1
 
1.4%
e1
 
1.4%
a1
 
1.4%
r1
 
1.4%
s1
 
1.4%

3. Present system of Treatment?
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
22 
medication
 
1

Length

Max length10
Median length3
Mean length3.304347826
Min length3

Characters and Unicode

Total characters76
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil22
95.7%
medication1
 
4.3%

Length

2022-10-25T20:08:52.604927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:52.931850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil22
95.7%
medication1
 
4.3%

Most occurring characters

ValueCountFrequency (%)
i24
31.6%
n23
30.3%
l22
28.9%
m1
 
1.3%
e1
 
1.3%
d1
 
1.3%
c1
 
1.3%
a1
 
1.3%
t1
 
1.3%
o1
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter76
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i24
31.6%
n23
30.3%
l22
28.9%
m1
 
1.3%
e1
 
1.3%
d1
 
1.3%
c1
 
1.3%
a1
 
1.3%
t1
 
1.3%
o1
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin76
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i24
31.6%
n23
30.3%
l22
28.9%
m1
 
1.3%
e1
 
1.3%
d1
 
1.3%
c1
 
1.3%
a1
 
1.3%
t1
 
1.3%
o1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII76
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i24
31.6%
n23
30.3%
l22
28.9%
m1
 
1.3%
e1
 
1.3%
d1
 
1.3%
c1
 
1.3%
a1
 
1.3%
t1
 
1.3%
o1
 
1.3%

4. Present Medications?
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
23 
ValueCountFrequency (%)
True23
100.0%
2022-10-25T20:08:53.230952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
15 
False
ValueCountFrequency (%)
True15
65.2%
False8
34.8%
2022-10-25T20:08:53.536890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
20 
True
ValueCountFrequency (%)
False20
87.0%
True3
 
13.0%
2022-10-25T20:08:53.867818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
22 
True
 
1
ValueCountFrequency (%)
False22
95.7%
True1
 
4.3%
2022-10-25T20:08:54.183769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
22 
True
 
1
ValueCountFrequency (%)
False22
95.7%
True1
 
4.3%
2022-10-25T20:08:54.514228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
False
22 
True
 
1
ValueCountFrequency (%)
False22
95.7%
True1
 
4.3%
2022-10-25T20:08:54.825948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
22 
10. are you suffering from any of the following?(depression; anxiety; adhd; bipolar disorder; schizophrenia etc)
 
1

Length

Max length112
Median length3
Mean length7.739130435
Min length3

Characters and Unicode

Total characters178
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil22
95.7%
10. are you suffering from any of the following?(depression; anxiety; adhd; bipolar disorder; schizophrenia etc)1
 
4.3%

Length

2022-10-25T20:08:55.147544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:55.486736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil22
59.5%
101
 
2.7%
are1
 
2.7%
you1
 
2.7%
suffering1
 
2.7%
from1
 
2.7%
any1
 
2.7%
of1
 
2.7%
the1
 
2.7%
following?(depression1
 
2.7%
Other values (6)6
 
16.2%

Most occurring characters

ValueCountFrequency (%)
i30
16.9%
n28
15.7%
l25
14.0%
14
 
7.9%
o9
 
5.1%
e9
 
5.1%
r8
 
4.5%
a6
 
3.4%
d5
 
2.8%
s5
 
2.8%
Other values (20)39
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter154
86.5%
Space Separator14
 
7.9%
Other Punctuation6
 
3.4%
Decimal Number2
 
1.1%
Open Punctuation1
 
0.6%
Close Punctuation1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i30
19.5%
n28
18.2%
l25
16.2%
o9
 
5.8%
e9
 
5.8%
r8
 
5.2%
a6
 
3.9%
d5
 
3.2%
s5
 
3.2%
f5
 
3.2%
Other values (12)24
15.6%
Other Punctuation
ValueCountFrequency (%)
;4
66.7%
?1
 
16.7%
.1
 
16.7%
Decimal Number
ValueCountFrequency (%)
01
50.0%
11
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin154
86.5%
Common24
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i30
19.5%
n28
18.2%
l25
16.2%
o9
 
5.8%
e9
 
5.8%
r8
 
5.2%
a6
 
3.9%
d5
 
3.2%
s5
 
3.2%
f5
 
3.2%
Other values (12)24
15.6%
Common
ValueCountFrequency (%)
14
58.3%
;4
 
16.7%
?1
 
4.2%
(1
 
4.2%
.1
 
4.2%
01
 
4.2%
11
 
4.2%
)1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i30
16.9%
n28
15.7%
l25
14.0%
14
 
7.9%
o9
 
5.1%
e9
 
5.1%
r8
 
4.5%
a6
 
3.4%
d5
 
2.8%
s5
 
2.8%
Other values (20)39
21.9%

11. How do you describe yourself?
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
16 
not applicable
(if sport person)
 
1

Length

Max length17
Median length3
Mean length6.47826087
Min length3

Characters and Unicode

Total characters149
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rownot applicable
2nd rownot applicable
3rd rownil
4th rownot applicable
5th rownot applicable

Common Values

ValueCountFrequency (%)
nil16
69.6%
not applicable6
 
26.1%
(if sport person)1
 
4.3%

Length

2022-10-25T20:08:55.936793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:56.337820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil16
51.6%
not6
 
19.4%
applicable6
 
19.4%
if1
 
3.2%
sport1
 
3.2%
person1
 
3.2%

Most occurring characters

ValueCountFrequency (%)
l28
18.8%
n23
15.4%
i23
15.4%
p14
9.4%
a12
8.1%
o8
 
5.4%
8
 
5.4%
t7
 
4.7%
e7
 
4.7%
c6
 
4.0%
Other values (6)13
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter139
93.3%
Space Separator8
 
5.4%
Open Punctuation1
 
0.7%
Close Punctuation1
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l28
20.1%
n23
16.5%
i23
16.5%
p14
10.1%
a12
8.6%
o8
 
5.8%
t7
 
5.0%
e7
 
5.0%
c6
 
4.3%
b6
 
4.3%
Other values (3)5
 
3.6%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin139
93.3%
Common10
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l28
20.1%
n23
16.5%
i23
16.5%
p14
10.1%
a12
8.6%
o8
 
5.8%
t7
 
5.0%
e7
 
5.0%
c6
 
4.3%
b6
 
4.3%
Other values (3)5
 
3.6%
Common
ValueCountFrequency (%)
8
80.0%
(1
 
10.0%
)1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l28
18.8%
n23
15.4%
i23
15.4%
p14
9.4%
a12
8.1%
o8
 
5.4%
8
 
5.4%
t7
 
4.7%
e7
 
4.7%
c6
 
4.0%
Other values (6)13
8.7%

(IF SPORT PERSON)
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
23 
ValueCountFrequency (%)
True23
100.0%
2022-10-25T20:08:56.693850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

What kind of sports
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing23
Missing (%)100.0%
Memory size312.0 B

(IF ATHLETE)
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing22
Missing (%)95.7%
Memory size312.0 B
nil

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rownil

Common Values

ValueCountFrequency (%)
nil1
 
4.3%
(Missing)22
95.7%

Length

2022-10-25T20:08:57.043356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:57.437129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil1
100.0%

Most occurring characters

ValueCountFrequency (%)
n1
33.3%
i1
33.3%
l1
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1
33.3%
i1
33.3%
l1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1
33.3%
i1
33.3%
l1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1
33.3%
i1
33.3%
l1
33.3%

Participating category(ies)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing23
Missing (%)100.0%
Memory size312.0 B

(IF FITNESS ENTHUSIAST)
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
20 
False
ValueCountFrequency (%)
True20
87.0%
False3
 
13.0%
2022-10-25T20:08:57.832072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Do you exercise regularly
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size151.0 B
True
23 
ValueCountFrequency (%)
True23
100.0%
2022-10-25T20:08:58.146379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

How many hours per week
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size312.0 B
nil
20 
cardio exercise
 
1
games
 
1
kind of exercises you prefer(yoga/weight training/cardio/cross training etc)
 
1

Length

Max length76
Median length3
Mean length6.782608696
Min length3

Characters and Unicode

Total characters156
Distinct characters25
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st rownil
2nd rownil
3rd rownil
4th rownil
5th rownil

Common Values

ValueCountFrequency (%)
nil20
87.0%
cardio exercise1
 
4.3%
games1
 
4.3%
kind of exercises you prefer(yoga/weight training/cardio/cross training etc)1
 
4.3%

Length

2022-10-25T20:08:58.504761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-25T20:08:58.921214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nil20
64.5%
cardio1
 
3.2%
exercise1
 
3.2%
games1
 
3.2%
kind1
 
3.2%
of1
 
3.2%
exercises1
 
3.2%
you1
 
3.2%
prefer(yoga/weight1
 
3.2%
training/cardio/cross1
 
3.2%
Other values (2)2
 
6.5%

Most occurring characters

ValueCountFrequency (%)
i30
19.2%
n25
16.0%
l20
12.8%
e11
 
7.1%
r9
 
5.8%
8
 
5.1%
s6
 
3.8%
c6
 
3.8%
a6
 
3.8%
o6
 
3.8%
Other values (15)29
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter143
91.7%
Space Separator8
 
5.1%
Other Punctuation3
 
1.9%
Open Punctuation1
 
0.6%
Close Punctuation1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i30
21.0%
n25
17.5%
l20
14.0%
e11
 
7.7%
r9
 
6.3%
s6
 
4.2%
c6
 
4.2%
a6
 
4.2%
o6
 
4.2%
g5
 
3.5%
Other values (11)19
13.3%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Punctuation
ValueCountFrequency (%)
/3
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin143
91.7%
Common13
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i30
21.0%
n25
17.5%
l20
14.0%
e11
 
7.7%
r9
 
6.3%
s6
 
4.2%
c6
 
4.2%
a6
 
4.2%
o6
 
4.2%
g5
 
3.5%
Other values (11)19
13.3%
Common
ValueCountFrequency (%)
8
61.5%
/3
 
23.1%
(1
 
7.7%
)1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i30
19.2%
n25
16.0%
l20
12.8%
e11
 
7.1%
r9
 
5.8%
8
 
5.1%
s6
 
3.8%
c6
 
3.8%
a6
 
3.8%
o6
 
3.8%
Other values (15)29
18.6%
Missing23
Missing (%)100.0%
Memory size312.0 B

Interactions

2022-10-25T20:07:44.786204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:34.589374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:36.538056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:38.923924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:40.892274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:42.861730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:45.100245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:34.936447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:36.864127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:39.239463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:41.210424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:43.169815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:45.426928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:35.257466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:37.211776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:39.569180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:41.546931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:43.516293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:45.743246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:35.583902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:37.864004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:39.913654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:41.883384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:43.851220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:46.058903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:35.904728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:38.254399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:40.247748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:42.215821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:44.159976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:46.362135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:36.221746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:38.582136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:40.563147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:42.534862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-25T20:07:44.469167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-25T20:08:59.282800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-25T20:09:00.030940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-25T20:09:00.796069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-25T20:09:02.107972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-25T20:09:06.934949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-25T20:07:48.139650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-25T20:07:52.199983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-25T20:07:53.070775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0.1Unnamed: 0NameAgeGenderPlaceHeightWeightBMIOccupationMarital StatusEthinicityDietAllergyAlcohol intakeSmoking CigarsAddiction(Others if any)Preffered cooking oilSaltSweetBlood pressureHbESRFasting blood sugarHbA1CTotal CholesterolHDLLDLTriglycerideTSHUric AcidUreaCreatinineSGOTSGPTPrescritpionLab-ReportX-raySkin allergies1.On a scale of 1-5, how much do you feel irritable if there is a delay in food or if a meal is missed?2. On a scale of 1-5, how much fatigue do you feel after having your meals?3. On a scale of 1-5 how will you rate your thirst on a normal day?4. On a scale of 1-5 how will you rate your appetite on a normal day?5. On a scale of 1-5 how much do you feel sluggish or less energetic(physically) these days?6. On a scale of 1-5, how often do you feel mentally sluggish these days?7. On a scale of 1-5, how much do you feel difficulty in focussing or difficulty in concentrating?8. On a scale of 1-5, how much do you feel sleepy during the day?1. On an average how many cups of tea/coffee do you have per day?2. How often do you skip your meals?3. How often do you feel sleeplessness at night4. How much time do you sleep in a day5. How often do you take fried foods/junk foods?6. How often do you experience mood swings?7. How often do you feel agitated or easily upset or nervous?7. How will you describe your ‘sex drive’ (Age limited)8. How often do you exercise?9. How will you describe your skin type?1. Do you feel trembling or palpitation, recurrently? Yes/No2. Do you have any variation in blood pressure? High BP/Low BP- (if the client has answered the qn no 21 in ‘Profile’, this question can be made hidden.)3. Is your waist circumference equal or larger than hip circumference? Yes/No4. Are you prone to fat deposition in the abdominal area? Yes/No5. Are you under a high amount of stress? Yes/No6. Do you experience any changes in your body weight, when you are under stress for a certain period? Yes/No → Weight Gain/Weight Los7. Are you gaining weight even with a low calorie diet? Yes/No8. Do you have difficulty in bowel movements/constipation? Yes/No9. Do you have excessive hair fall, these days? Yes/No10. Do you sweat too much while sleeping ? Yes/No11. Do you have difficulty in gaining/losing weight ? Yes/No; If yes→ for gaining/losing12. Do you have difficulty in digesting fruits and vegetables; undigested food found in stools? Yes/No13. Do you have excessive burping/ belching/ bloating? Yes/No14. Do you have difficulty in learning new things ? Yes/No15. Do you sweat/perspire excessively, with little or no activity? Yes/No16. Do you experience tremor/shaking while your legs or hands are at rest ? Yes/No17. Do you feel any giddiness on standing for a long time? Yes/No18. Do you feel your memory is poor or are you forgetful? Yes/No19. Do you feel difficulty in spelling correctly, familiar words? Yes/No20. Has there any change in vision (eye sight) recently ? Yes/No21. Is there any difficulty in making decisions or co-ordinating ?22. Do you feel depressed or lack motivation?23. Are you more emotional now-a days, than in the past?24. Does your frequency of urination increased these days?1. Do you feel any urgency in urination, these days? Yes/No2. Do you feel any decrease in fullness of erections? Yes/No3. Do you have any difficulty in maintaining morning erections? Yes/No4. Do you feel any decrease in physical stamina? Yes/No5. Do you have recurrent premature ejaculations?6. Do you have any increase in fat distribution around the chest and hips? Yes/NoA. Age of MenarcheB. Cycle lengthC. RegularityD. Days of flowE. Quantity of bleeding- Heavy/Normal/scantyF. ClotsG. Abnormal dischargesH. Associated symptoms- Pains/PMS/cramps/Low Back pain/ Mood Swings/Depressed2. Do you have any abnormal hair growth? (eg: in face, chest etc)3. Are you suffering from Pimples/Acne?4. Are you suffering from hair loss?MENOPAUSAL FEMALES ONLY1. Age of Menopause2. Do you have any bleeding after menopause? Yes/No3. Do you have any disinterest in sex? Yes/No4. Are you suffering from severe mood swings? Yes/No5. Do you feel any hot flushes? Yes/No1. Do you feel forgetfulness, these days?2. Do you feel it is difficult to be attentive in your studies?3. Do you feel sleepy during study time?4. How will you describe yourself? Sluggish/Active/Hyperactive1. Are you suffering from any lifestyle diseases of the following?2. How long are you suffering?3. Present system of Treatment?4. Present Medications?5. Do you have any Family History of the following diseases?6. Have you undergone any major surgery(ies)? Yes/No; if yes please specify7. Are you suffering from any of the following symptoms, these days?8. Do you have any Drug allergies?9. Are you having any form of physical disability?10. Are you suffering from any of the following?(Depression; Anxiety; ADHD; Bipolar Disorder; Schizophrenia etc)11. How do you describe yourself?(IF SPORT PERSON)What kind of sports(IF ATHLETE)Participating category(ies)(IF FITNESS ENTHUSIAST)Do you exercise regularlyHow many hours per weekKind of exercises you prefer(yoga/weight training/Strength Training/Functional Movements/cardio/cross training etc)
03232arya tara rai7fdarjeeling1707023doctormarriedethinicitynon-veg(readmeat)nonilnilnilsunflower oil,mustard oilnot interestedcravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalNaNNaNNaNno1.01.01.03.01.01.05.01.00.0not at allnot at alld. more than 8 hoursnot at allnot at allnot at allNaNnot at allnormalnoyesnonononononononononononononononononononononoyesyesyesyesyesNaN13.0normalregular4.0normalnonopain,cramps,depressednoyesnoNaNNaNnononoyesnoyesnosluggishnonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
16666sriya sreenivasan7fnil1223523doctormarriedethinicitynon-veg(readmeat)nonilnilnilnilcravescraveslowlowhighhighhighhighlowhighlowhighlowlowlowlowhighNaNNaNNaNno5.01.01.01.01.01.01.01.00.0not at allnot at alld. more than 8 hoursfrequentlynot at allnot at allNaNmore than 5 hours a weeknormalnoyesyesyesyesyesyesyesyesnonononoyesnonononononoyesnononoyesyesyesyesyesNaNnilnilregularnilnilnononilyesnonoNaNNaNnononoyesyesyesnoactivenonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
2109109shitanshu verma5mnil1707023doctormarriedethinicityvegeterian(green leafy)nonilnilnilmustard oilcravescraveslowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes1.01.05.05.05.01.05.01.00.0frequentlynot at allc. 6-8 hoursfrequentlynot at allfrequentlyNaNnot at allNaNnoyesyesyesnononononoyesyesnonoyesyesnonoyesnonoyesnoyesnoyesyesyesyesyesNaNnilnilregularnilnilnononilyesnoyesNaNNaNnoyesnonoyesyesnoactivenonilnilyesyesnononononilnilyesNaNNaNNaNyesyesnilNaN
3151151rizanizar10fnil1302523doctormarriedethinicitynon-veg(chicken)yesnilnilnilcoconut oil ,olive oil and sunflower oil (rarely)not interestednot interestedlowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes1.01.01.01.01.01.01.01.00.0frequentlyfrequentlya. less than 5 hoursfrequentlynot at allnot at allNaNnot at allNaNnoyesnonononoyesyesnononononoyesnonononononoyesnononoyesyesyesyesyesNaNnilnilregularnilnilnononilnoyesyesNaNNaNyesyesnonoyesyesnoactivenonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
4164164iffa ahmed10fnil1493823doctormarriedethinicitynon-veg(chicken)nonilnilnilrice bran oilcravescraveslowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes5.01.05.01.05.05.05.01.00.0not at allfrequentlya. less than 5 hoursnot at allfrequentlyfrequentlyi feel i have normal sex drivenot at alldryyesyesnonononononoyesyesnonononoyesnoyesnononoyesyesyesyesyesyesyesyesyesNaNnilnilregularnilnilnononilyesyesyesNaNNaNyesyesnonoyesyesnoactivenonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
5173173sheetal kumari6fnil1212523doctormarriedethinicitynon-veg(chicken)nonilnilnilrice bran oilcravesnot interestedlowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes5.01.01.01.01.01.01.01.00.0not at allnot at alld. more than 8 hoursnot at allnot at allfrequentlyNaNmore than 5 hours a weeknormalnoyesnonononononononononononononononononoyesnononoyesyesyesyesyesNaNnilnilregularnilnilnononilyesyesyesNaNNaNyesyesnonoyesyesnoactivenonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
6190190devasree8fnil1112723doctormarriedethinicitynon-veg(chicken)nonilnilnilcoconut oilcravesnot interestedlowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes1.01.01.01.01.01.05.01.00.0not at allnot at alld. more than 8 hoursfrequentlynot at allfrequentlyNaNnot at allnormalnoyesnonoyesnonoyesnoyesyesnononoyesnononononoyesnononoyesyesyesyesyesNaNnilless than 24 daysregularnilnilnononilyesyesyesNaNNaNyesyesnonoyesyesnoactivenonilnilyesyesnononononilnot applicableyesNaNNaNNaNyesyesnilNaN
7223223amrit t krishnadas11mnil1362514doctormarriedethinicitynon-veg(chicken)nonilnilnilcoconut oilcravesnot interestedlowlowlowlowlowlowlowlowlowlowlowlowlowlowlowNaNNaNNaNyes5.01.01.01.01.01.01.01.00.0not at allnot at alld. more than 8 hoursfrequentlynot at allnot at allNaNnot at alldrynoyesnononoyesnonoyesyesyesnononononononononoyesnononoyesyesyesyesyesNaNnilnilregularnilnilnononilnonoyesNaNNaNyesyesnonoyesyesnoactivenonilnilyesnonononononilnilyesNaNNaNNaNyesyesnilNaN
8282283sriya sreenivasan7fnil1223524nilunmarriedasainnon-veg(readmeat)no1.01.0nonilcravescravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononono5.01.01.02.01.01.01.02.00.0not at allnot at allc. 6-8 hoursfrequentlynot at allnot at alli feel i have normal sex drive2-3 hours a weeknormalnoyesyesyesnonoyesnononononononononononononononononoyesyesyesyesyesNaNnilnilregularnilnilyesyesnilyesnonoNaNNaNnonononoyesyesnoactiveyes4 yearsmedicationyesyesnononononilnilyesNaNNaNNaNyesyesnilNaN
9373374sabrina gurung11fnil1707023nilunmarriedasainnon-veg(readmeat)no1.01.0nomustard oilcravescravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononono5.01.05.04.01.01.01.01.00.0not at allnot at allc. 6-8 hoursfrequentlynot at allfrequentlyi feel i have normal sex drivenot at alldrynoyesnonoyesyesyesnononononononononononoyesnononononoyesyesyesyesyesNaN11.0normalregular4.0heavyyesyespain,cramps,nononoNaNNaNnonononoyesyesnoactivenonilnilyesyesnononononilnilyesNaNNaNNaNyesyesnilNaN

Last rows

Unnamed: 0.1Unnamed: 0NameAgeGenderPlaceHeightWeightBMIOccupationMarital StatusEthinicityDietAllergyAlcohol intakeSmoking CigarsAddiction(Others if any)Preffered cooking oilSaltSweetBlood pressureHbESRFasting blood sugarHbA1CTotal CholesterolHDLLDLTriglycerideTSHUric AcidUreaCreatinineSGOTSGPTPrescritpionLab-ReportX-raySkin allergies1.On a scale of 1-5, how much do you feel irritable if there is a delay in food or if a meal is missed?2. On a scale of 1-5, how much fatigue do you feel after having your meals?3. On a scale of 1-5 how will you rate your thirst on a normal day?4. On a scale of 1-5 how will you rate your appetite on a normal day?5. On a scale of 1-5 how much do you feel sluggish or less energetic(physically) these days?6. On a scale of 1-5, how often do you feel mentally sluggish these days?7. On a scale of 1-5, how much do you feel difficulty in focussing or difficulty in concentrating?8. On a scale of 1-5, how much do you feel sleepy during the day?1. On an average how many cups of tea/coffee do you have per day?2. How often do you skip your meals?3. How often do you feel sleeplessness at night4. How much time do you sleep in a day5. How often do you take fried foods/junk foods?6. How often do you experience mood swings?7. How often do you feel agitated or easily upset or nervous?7. How will you describe your ‘sex drive’ (Age limited)8. How often do you exercise?9. How will you describe your skin type?1. Do you feel trembling or palpitation, recurrently? Yes/No2. Do you have any variation in blood pressure? High BP/Low BP- (if the client has answered the qn no 21 in ‘Profile’, this question can be made hidden.)3. Is your waist circumference equal or larger than hip circumference? Yes/No4. Are you prone to fat deposition in the abdominal area? Yes/No5. Are you under a high amount of stress? Yes/No6. Do you experience any changes in your body weight, when you are under stress for a certain period? Yes/No → Weight Gain/Weight Los7. Are you gaining weight even with a low calorie diet? Yes/No8. Do you have difficulty in bowel movements/constipation? Yes/No9. Do you have excessive hair fall, these days? Yes/No10. Do you sweat too much while sleeping ? Yes/No11. Do you have difficulty in gaining/losing weight ? Yes/No; If yes→ for gaining/losing12. Do you have difficulty in digesting fruits and vegetables; undigested food found in stools? Yes/No13. Do you have excessive burping/ belching/ bloating? Yes/No14. Do you have difficulty in learning new things ? Yes/No15. Do you sweat/perspire excessively, with little or no activity? Yes/No16. Do you experience tremor/shaking while your legs or hands are at rest ? Yes/No17. Do you feel any giddiness on standing for a long time? Yes/No18. Do you feel your memory is poor or are you forgetful? Yes/No19. Do you feel difficulty in spelling correctly, familiar words? Yes/No20. Has there any change in vision (eye sight) recently ? Yes/No21. Is there any difficulty in making decisions or co-ordinating ?22. Do you feel depressed or lack motivation?23. Are you more emotional now-a days, than in the past?24. Does your frequency of urination increased these days?1. Do you feel any urgency in urination, these days? Yes/No2. Do you feel any decrease in fullness of erections? Yes/No3. Do you have any difficulty in maintaining morning erections? Yes/No4. Do you feel any decrease in physical stamina? Yes/No5. Do you have recurrent premature ejaculations?6. Do you have any increase in fat distribution around the chest and hips? Yes/NoA. Age of MenarcheB. Cycle lengthC. RegularityD. Days of flowE. Quantity of bleeding- Heavy/Normal/scantyF. ClotsG. Abnormal dischargesH. Associated symptoms- Pains/PMS/cramps/Low Back pain/ Mood Swings/Depressed2. Do you have any abnormal hair growth? (eg: in face, chest etc)3. Are you suffering from Pimples/Acne?4. Are you suffering from hair loss?MENOPAUSAL FEMALES ONLY1. Age of Menopause2. Do you have any bleeding after menopause? Yes/No3. Do you have any disinterest in sex? Yes/No4. Are you suffering from severe mood swings? Yes/No5. Do you feel any hot flushes? Yes/No1. Do you feel forgetfulness, these days?2. Do you feel it is difficult to be attentive in your studies?3. Do you feel sleepy during study time?4. How will you describe yourself? Sluggish/Active/Hyperactive1. Are you suffering from any lifestyle diseases of the following?2. How long are you suffering?3. Present system of Treatment?4. Present Medications?5. Do you have any Family History of the following diseases?6. Have you undergone any major surgery(ies)? Yes/No; if yes please specify7. Are you suffering from any of the following symptoms, these days?8. Do you have any Drug allergies?9. Are you having any form of physical disability?10. Are you suffering from any of the following?(Depression; Anxiety; ADHD; Bipolar Disorder; Schizophrenia etc)11. How do you describe yourself?(IF SPORT PERSON)What kind of sports(IF ATHLETE)Participating category(ies)(IF FITNESS ENTHUSIAST)Do you exercise regularlyHow many hours per weekKind of exercises you prefer(yoga/weight training/Strength Training/Functional Movements/cardio/cross training etc)
13552555kezia johnson10fnil1453416occupationmarriedethinicitynon-veg(chicken)nonilnilnococonut oilnot interestedcravesnormallownormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononoyes1.01.01.01.05.01.01.01.00.0frequentlynot at allc. 6-8 hoursnot at allnot at allfrequentlyi feel i have normal sex driveonce in a whilenormalnoyesyesnononononononononononononononononononononoyesyesyesyesyesNaN13.0normalregular4.0normalyesyesyesnonoyesNaNNaNnononononononoactivenonilnilyesyesnononononilnilyesNaNNaNNaNnoyescardio exerciseNaN
14563566ved dharmendra dhanani10mnil1222818businessmarriedethinicityvegeterian(cereals)NaNnilnilnorice bran oilcravescravesnormallowlownormalnormalnormallowhighhighnormalnormalnormalnormalnormallownononono5.01.05.05.05.01.01.01.00.0not at allfrequentlyc. 6-8 hoursnot at allfrequentlyfrequentlyi feel i have normal sex drivenot at allnormalnoyesnonononononononononononononoyesnoyesnonoyesyesnoyesyesyesyesyesNaNnilnilregularnilnilnononilnononoNaNNaNnoyesnoyesnononoactivenonilnilyesyesnononononilnilyesNaNNaNNaNnoyesnilNaN
15666669miss senora menezes5fnil1707023businessmarriedethinicityNaNNaNnilnilnilnilcravescravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalNaNNaNNaNyes5.01.01.05.05.01.01.01.00.0not at allnot at allNaNfrequentlyfrequentlyfrequentlyi feel i have normal sex drive2-3 hours a weeknormalnoyesyesnonoyesnoyesyesnoyesnononononononononoyesyesyesnoyesyesyesyesyesNaNnilnilregularnilnilnononilyesnonoNaNNaNnononononononoactiveyesnilnilyesyesyesnonononilnilyesNaNNaNNaNyesyesnilNaN
16673676riyana parveen p s9fnil1402223businessmarriedethinicitynon-veg(chicken)NaNnilnilnilcoconut oilcravescravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalNaNNaNNaNyes1.01.01.05.01.01.01.01.00.0frequentlynot at allNaNnot at allnot at allnot at alli feel i have normal sex drive2-3 hours a weekdrynoyesnononononoyesyesnoyesnononononononononoyesnononoyesyesyesyesyesNaNnilnormalregularnilnilnononilyesyesyesNaNNaNnononononononoactiveyesnilnilyesyesyesnonononilnilyesNaNNaNNaNyesyesgamesNaN
17724728k shanker6mnil1707023doctormarriedethinicityvegeterian(cereals)yesnilnilnilnilnot interestedcravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononono1.01.01.03.01.01.01.01.00.0not at allnot at allc. 6-8 hoursnot at allnot at allfrequentlyi have increased sex drivemore than 5 hours a weeknormalnoyesnoyesnononononononononononononoyesnoyesnononononononononononilnilregularnilnilnononilnononoNaNNaNnoyesnonoyesyesyes4. how will you describe yourself? sluggish/active/hyperactivenonilnilyesnonononononilnilyesNaNNaNNaNyesyesnilNaN
188071124nemy joseph7mbanglore1282123occupationmarriedethinicitynon-veg(readmeat)nonilnilnilcoconut oilnot interestedcravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalNaNNaNNaNyes5.01.01.03.01.01.05.02.00.0not at allnot at allc. 6-8 hoursnot at allnot at allfrequentlyi feel i have normal sex drive2-3 hours a weekdrynoyesnonononononononoyesnonononononononononononoyesyesyesyesyesyesNaNnilnilregularnilnilnononilnonoyesNaNNaNyesyesyesnonoyesyesactivenonilnilyesnonononononilnilyesNaNNaNNaNyesyesnilNaN
199102496laxmi-stb21002256711fnorth india1606023nilunmarriedasainvegeterian(cereals)nonilnilnilrefined oilpreferscravesnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononoyes5.01.05.03.05.05.01.03.00.0not at allfrequentlyb. 5 -6 hoursnot at allfrequentlyfrequentlyno sex drive these days2-3 hours a weekcombination of dry and oilynoyesnonononononononononononononononoyesnononononoyesyesyesyesyesNaNa. age of menarcheb. cycle lengthregulard. days of flowe. quantity of bleeding- heavy/normal/scantyyesnoh. associated symptoms- pains/pms/cramps/low back pain/ mood swings/depressednoyesyesNaNNaNnononononoyesnoactivenonilnilyesnonononono10. are you suffering from any of the following?(depression; anxiety; adhd; bipolar disorder; schizophrenia etc)(if sport person)yesNaNNaNNaNyesyeskind of exercises you prefer(yoga/weight training/cardio/cross training etc)NaN
209583209jiya r rakholiya _gfx02056838fnorth india1707023studentunmarriedasainvegeterian(cereals)no1.01.01.0peanut oilcravesnot interestednormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononoyes5.01.01.03.01.01.05.01.00.0not at allnot at allc. 6-8 hoursfrequentlynot at allfrequentlyi feel i have normal sex drivenot at allnormalnoyesnoyesnononoyesnonoyesnonoyesnononoyesyesyesnonononoyesyesyesyesyesNaN13.0normalirregular2022-07-05 00:00:00heavynonoyesyesnoyesNaNNaNnonononoyesyesyessluggishnonilnilyesnonononononilnilyesNaNNaNNaNyesyesnilNaN
2110043740viraj vishal bekellu_gfx02056878msouth india1202517studentunmarriedasainnon-veg(chicken)no1.01.01.0mustard oilprefersprefersnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononoyes1.01.01.01.01.01.01.01.00.0not at allnot at alla. less than 5 hoursnot at allnot at allfrequentlyi feel i have normal sex driveonce in a whileoilynoyesnonononononononononononononononononononononoyesyesyesyesyesNaNnilnilregularnilnilyesnonilyesyesyesNaNNaNnononononononosluggishnonilnilyesnonononononilnilyesNaNNaNNaNyesyesnilNaN
2210304072k prabhass10mkarnataka1212818studentunmarriedasainnon-veg(chicken)no1.01.0nilcoconut oilcravesnot interestednormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnormalnononoyes5.01.01.03.01.05.05.05.00.0not at allfrequentlyb. 5 -6 hoursfrequentlynot at allfrequentlyi feel i have normal sex drivenot at allnormalnoyesnononononononoyesnononoyesnonononononoyesnononoyesyesyesyesyesNaNnilnilregularnilnilyesnonilnoyesyesNaNNaNnononononoyesnohyperactivenonilnilyesnonononononilnilyesNaNnilNaNnoyesnilNaN